One challenge you may encounter as a data scientist is ensuring your computer vision models are not biased towards any particular demographic group. Our comprehensive guide offers practical techniques and resources to help you implement fairness and mitigate bias in your AI systems.
Structure, write, and edit your data scientist CV to land the role of your dreams with our exclusive guide on how to:
- Create an attention-grabbing header that integrates keywords and includes all vital information;
- Add strong action verbs and skills in your experience section, and get inspired by real-world professionals;
- List your education and relevant certification to fill in the gaps in your career history;
- Integrate both hard and soft skills all through your CV.
Discover more industry-specific guides to help you apply for any role in the links below:
Resume examples for data scientist
Explore additional data scientist cv samples and guides and see what works for your level of experience or role.
By Experience
Junior Data Scientist
- Clarity and Structured Presentation - The CV is well-structured, starting with a clear header providing essential contact information. It moves logically through sections, using concise bullet points to detail experience and achievements, ensuring readability and easy navigation for potential employers.
- Dynamic Career Growth in Advanced Analytics - Scarlett Murphy demonstrates steady career progression from a Data Analyst Intern to a Junior Data Scientist within a short span, indicating strong industry growth and an increasing depth of expertise. This trajectory showcases her ability to quickly acquire new skills and add increasing value to her roles.
- Technical Expertise with Predictive Models - The CV highlights specific industry-standard tools and methodologies such as AWS, R, Python, and Tableau, showing technical proficiency. The focus on predictive modeling and cloud solutions integration underscores a deep understanding of data science technologies crucial for the role of Junior Data Scientist.
Senior Data Scientist
- Clear and logical structure - The CV is meticulously organized, starting with a concise summary followed by sections that seamlessly flow into one another—experience, skills, education, and certifications. This arrangement not only highlights the key competencies and qualifications effectively but also makes it easy for the employer to quickly locate pertinent information.
- Demonstrates evolving responsibilities and industry relevance - The CV reflects a dynamic career trajectory with progressive roles in the renewable energy sector, showcasing a clear path from entry-level engineering roles to strategic leadership positions. The candidate's journey indicates not only personal growth but also a commendable alignment with industry trends and needs.
- Highlights specialized software and methodologies - The document impressively lists expertise in specific industry tools such as AutoCAD, MATLAB, and Finite Element Analysis, which are vital in the engineering sector. Additionally, it references methodologies like Lean Six Sigma, underscoring the candidate’s commitment to efficiency and quality in project execution.
Lead Data Scientist
- Structured presentation highlights key skill areas - The CV is clearly and concisely structured, providing sections like experience, education, skills, and achievements that pertain directly to the role of a Lead Data Scientist. Each section is designed to underscore the candidate's qualifications and seamlessly guide the reader through their career journey.
- Demonstrates robust career progression - Finley Robinson's career trajectory showcases a steady rise from Data Scientist to Senior Data Scientist, culminating in the position of Lead Data Scientist. This upward movement reflects significant professional growth and recognition, indicating his capability to assume increasing responsibilities and lead effectively.
- Emphasizes strategic leadership and collaboration skills - The CV effectively highlights Finley's capacity for leading diverse teams and mentoring junior members, alongside his ability to engage stakeholders and align project outcomes with business objectives. His experience in cross-functional collaboration further underscores his adaptability in complex environments.
Principal Data Scientist
- Structured and Detail-Oriented Presentation - Florence Knight's CV is meticulously organized, making it easy to navigate through her vast experience and achievements. Each section is clearly defined with concise bullet points that allow the reader to quickly absorb key information. This clarity enhances the overall comprehension of her qualifications and career highlights.
- Strong Career Progression in Data Science - The career trajectory is impressive, showing a natural progression from Data Scientist to Principal Data Scientist. Her career path reflects significant growth within reputable companies in the UK, demonstrating her ability to gain trust and responsibility while advancing through the ranks to leadership positions.
- Proficient Use of Advanced Tools and Techniques - Florence showcases her expertise in industry-specific tools such as TensorFlow, PyTorch, and GCP, along with methodologies like deep generative modeling and CI/CD processes. This technical depth indicates her capability to handle complex data challenges and stay updated with cutting-edge technologies.
Data Science Team Leader
- Structured and Concise Presentation - The CV is well-organized with clearly defined sections, making it easy for readers to navigate through the candidate's career details. The use of concise bullet points effectively highlights the key responsibilities and achievements in each role without overwhelming the reader with unnecessary information.
- Progressive Career Development - The career trajectory of Sophia Foster demonstrates a clear progression from a Data Analyst to a Senior Data Engineer and eventually a Team Leader. This ascent underscores her ability to adapt and grow within the industry, particularly moving into leadership and strategic roles while maintaining her technical foundation.
- Emphasis on Modern Data Practices and Technologies - The CV showcases a robust understanding of contemporary data engineering methodologies, including ETL/ELT processes, CI/CD practices, and data modeling. The technical skills section reflects expertise in a variety of tools such as Oracle and MySQL, which reinforces her capability to handle complex data integrations and analytics tasks efficiently.
By Role
Data Science Project Manager
- Well-Structured and Clear Presentation - The CV excels in delivering a clear and concise presentation by neatly organizing information into sections like experience, education, skills, and achievements. The content is structured to provide a quick yet comprehensive overview of Archie's career, making it easy to assess his qualifications at a glance.
- Impressive Career Growth and Industry Shifts - Archie's career trajectory reflects both promotions and industry shifts that illustrate his capacity for growth. Starting from a Project Manager at British Gas to a Portfolio Manager at National Grid, his path reveals not only a rise in responsibility but also adaptability across different sectors within the energy industry.
- Leadership and Cross-functional Collaboration - The CV highlights Archie's leadership skills, especially in cross-functional team settings. His experience in engaging with C-level stakeholders and fostering collaboration among multifaceted teams stands out as a testament to his ability to lead diverse groups toward common goals, enhancing overall project efficiency and effectiveness.
Data Science Consultant
- Emphasizes Strategic Use of Data Science Methodologies - The CV excels in showcasing the candidate's deep understanding of data science tools and methodologies. Their expertise in building predictive models and conducting complex analyses using tools such as Random Forests and Monte Carlo simulations is highlighted as pivotal to enhancing decision-making processes and forecasting market trends.
- Demonstrates Continuous Career Growth and Advancement - James Lewis's career trajectory is effectively presented, showing a clear progression from a Junior Data Analyst at KPMG to a Data Scientist role at Deloitte. Each career move marks an elevation in responsibilities and impact, illustrating professional growth and commitment to advancing within the data science field.
- Adaptability through Cross-Functional Collaboration - The candidate's ability to collaborate across different teams is evident through roles involving cross-functional project leadership and close work with diverse stakeholders. Their capacity to communicate complex data insights effectively to non-technical audiences is a testament to their adaptability and interpersonal skills.
Data Science Engineer
- Structured and Cohesive Presentation - The CV is organized in a way that facilitates easy understanding of Sophia Brooks' qualifications. Each section is clearly demarcated and presents information concisely, allowing for quick absorption of key details such as her skills in Python, R, and data visualization. This clarity in content organization significantly aids in highlighting her technical capabilities.
- Progressive Career Development - Sophia's career trajectory shows a clear path of advancement from a Junior Data Scientist to a Senior Data Science Engineer. The transition reflects an increasing level of responsibility and expertise in condition monitoring and analytics, indicating her growth and development within her field over an 8-year period.
- Industry-Relevant Achievements - The CV emphasizes tangible achievements, like a 30% improvement in predictive maintenance at Transport for London and optimization of data processing algorithms by 40%. These accomplishments not only highlight Sophia's technical proficiency but also demonstrate her ability to deliver business-critical improvements, impacting customer satisfaction and operational efficiency positively.
Data Science Researcher
- Clear and Organized Presentation - The CV is structured with precision, offering a clear view of the individual's professional pathway. It employs concise, impactful language across sections like summary, experience, and education, enabling quick comprehension of significant career highlights and competencies.
- Impressive Career Progression - Freya's career trajectory reveals consistent growth and upward mobility, from a Bioinformatics Analyst to a Lead Data Scientist role. This progression underscores her aptitude for leadership and increasing responsibility, particularly in renowned organizations like GSK and Oxford Nanopore Technologies.
- Industry-Specific Expertise - Freya demonstrates an impressive depth of knowledge in genomics and cell biology, enhanced through the application of tools and methodologies like RNA-seq, KEGG Database integration, and multi-omics data models. This indicates not only technical proficiency but also innovation in leveraging these tools for tangible scientific advancements.
Data Science Instructor
- Strong content organization - The CV efficiently uses sectional headings like "Experience", "Education", and "Skills" to compartmentalize information, allowing for easy navigation and clarity. This approach ensures that key details can be quickly located by potential employers.
- Diverse career trajectory - Jack Taylor’s career demonstrates clear growth from a Data Analyst to a Lead Data Scientist. This progression highlights his ability to take on increasing levels of responsibility and showcases his determination to develop professionally within the field of data science.
- Integration of advanced tools - The CV highlights Jack's proficiency with industry-standard tools such as Python, R, and SQL, reflecting a deep technical expertise. These skills are crucial for data analysis and underline his capability to handle complex data-related projects effectively.
Data Science Product Manager
- Strategic Career Progression - Finley Robinson's CV showcases a strategic career trajectory with a clear growth pattern from a Data Analyst to a Product Manager within reputable companies such as BP and Wood Mackenzie. This progression highlights not just an increase in responsibilities but also an expanding expertise in bridging data science with product management, reflecting a well-devised career path that emphasizes leadership and adaptability.
- Cross-Functional Leadership and Stakeholder Management - The CV emphasizes Robinson's strong leadership skills, demonstrated through his role in leading cross-functional teams and managing stakeholder expectations, achieving an impressive 98% satisfaction rate. The capacity to manage multiple projects concurrently and avoid feature creep showcases robust organizational skills and effective stakeholder communication, crucial for success in product management.
- Impactful Achievements with Business Relevance - Robinson's achievements are not just about numbers but their impact on business objectives, such as enhancing market forecasting accuracy by 25% and reducing development cycle time by 15%. These accomplishments demonstrate a deep understanding of business needs and the ability to drive growth and efficiency, ensuring that his contributions align with and propel organizational goals.
Machine Learning Data Scientist
- Structured for Readability - The CV's layout is intelligently organized into sections that allow for seamless navigation. The use of bullet points and headings increases clarity, facilitating the easy identification of key points and achievements. Such structure is particularly effective for busy recruiters, allowing them to quickly assess the candidate’s suitability for the role.
- Progressive Career Development - The candidate’s career journey reflects deliberate upward mobility, with successive roles showcasing enhanced responsibilities and leadership over projects. This trajectory from a junior analyst to a senior strategist highlights both personal growth and an evolving depth of industry understanding, suggesting a capacity for scaling contributions in future roles.
- Integration of Cross-Functional Proficiencies - The CV demonstrates versatility through the inclusion of varied experiences across departments. From collaborating with design teams to working alongside finance professionals, the narrative underscores the candidate’s ability to navigate and contribute meaningfully in multidisciplinary environments, a desirable trait in versatile roles.
Data Science Architect
- Concise and Well-Structured Content Presentation - Ava Phillips' CV effectively makes use of a clear and concise format. Each section is well-organized, providing a snapshot of relevant information that is easily digestible for the reader. Bullet points under each job role highlight key responsibilities and achievements, which not only adds clarity but also ensures that critical information is not overlooked.
- Strong Career Trajectory with Increased Responsibility - The career progression from a Business Intelligence Analyst to a Data Architect illustrates a clear growth trajectory. This upward movement reflects growing responsibilities, leadership capabilities, and a consistent dedication to professional development. Each role builds on the last, demonstrating an expanding skill set and deeper industry knowledge.
- Proficient Use of Industry-Specific Technologies and Tools - The document delineates a strong command over industry-standard tools and methodologies, such as AWS BI products, Power BI, and SQL. Ava's proficiency in data warehousing, data modeling, and dashboard creation speaks to her technical depth, making her a prime candidate for roles demanding high technical expertise in data architecture and business intelligence.
Data Science Operations Manager
- Strong Vision of Leadership and Team Management - Ruby Gibson's CV clearly portrays her strategic leadership, demonstrated by managing teams of up to 20 technical professionals and driving a 25% increase in data center efficiency. This showcases her ability to not only lead but inspire innovation within her team to optimize processes effectively.
- Career Growth and Industry Adaptation - The progression from Technical Operations Manager to Site Operations Manager evidences her upward career trajectory, reflecting her adaptability and growing expertise in data center operations. Ruby's ability to manage increasing budgets and her engagement in strategic vendor management highlight significant career advancement and capability to handle larger responsibilities.
- Environment Focus and Implementation of Sustainability Practices - Unique to her background, Ruby has initiated environmentally-friendly practices, reducing energy consumption by 15% annually. This is an impressive commitment to sustainability in an industry known for its energy demands, adding a specialized layer to her skill set that aligns with modern business goals of environmental responsibility.
Data Science Quality Assurance Specialist
- Strategized Career Growth - Oliver's career trajectory showcases a clear path of professional development, moving from a Clinical Research Associate to a senior specialist, indicating both ambition and industry recognition. His upward movement within well-established pharmaceutical companies highlights his skills in Good Clinical Practice and Quality Assurance.
- Industry-Specific Certifications - The CV features relevant courses such as "Advanced Good Clinical Practice" and "Pharmacovigilance and Risk Management Planning," which indicate ongoing professional development and a commitment to mastering compliance and risk management in the pharmaceutical industry.
- Leadership and Innovation in Quality Systems - Oliver demonstrates leadership through his role in reducing non-compliance incidents by 30% and revising training systems to increase satisfaction and reduce errors. Such achievements signify his capability to innovate and enhance quality systems effectively.
Data Science Coordinator
- Structure and Clarity in Content Presentation - The CV stands out by providing a well-organized structure, beginning with a concise yet informative summary that lays the groundwork for the candidate's professional background. Each job role is clearly outlined with distinct bullet points that highlight specific responsibilities and achievements, allowing for effortless navigation through the document.
- Diverse Career Trajectory - William Hall’s career reflects significant growth and a strong upward trajectory within the field of science. Progressing from a Research Assistant to a Research Laboratory Manager, he demonstrates a commitment to advancing his expertise and leadership abilities, marked by his role at a reputable company like Oxford Biomedica.
- Application of Advanced Laboratory Tools and Methodologies - This CV highlights the use of sophisticated industry-specific tools such as CRISPR/Cas9 and electronic laboratory notebooks. These elements emphasize William's technical depth and his capability to integrate cutting-edge research methodologies into everyday lab operations, which are crucial for a Research Laboratory Manager in Molecular Biology.
By Industry
Clinical Data Scientist
- Structured and Concise Presentation - The CV is exceptionally clear and well-organized, facilitating ease of reading through its structured format. It effectively distinguishes each section, allowing the reader to quickly identify areas of expertise, education, and professional experience. This clarity ensures that the critical information is conveyed without the unnecessary clutter, maintaining focus on core accomplishments and skills.
- Progressive Career Growth in Vaccine Research - The CV articulates a well-defined career trajectory, showcasing impressive growth from a Data Analyst to a Lead Data Scientist. Each position reflects an advancement in responsibility and expertise, particularly within clinical trials and vaccine research. This upward mobility, paired with a specific focus on impactful public health projects like vaccine trials, underscores a deep industry specialization.
- Integration of Advanced Tools and Methodologies - Highlighting proficiency in high-level tools such as R, Python, and SAS, the CV illustrates Finley Robinson's technical capacity to execute sophisticated statistical models and bioinformatics analyses. The repeated mention of machine learning, data visualization, and predictive modeling emphasizes a strong foundation in data science, tailored specifically to the needs of epidemiological research and clinical trials.
Financial Data Scientist
- Clear career progression and sector focus - Scarlett Murphy's CV demonstrates a clear and impressive trajectory in the data science field, evolving from a Junior Data Analyst to an Associate Data Scientist. This growth highlights not only her increasing responsibilities but also her steadfast commitment to finance and risk analysis, as seen through her roles in prominent financial institutions like Barclays, HSBC, and Lloyds Bank.
- Insight into industry-specific technical capabilities - The CV distinctly showcases Scarlett's proficiency in vital industry tools and techniques, such as Python, SAS, and SQL. Her expertise in machine learning methodologies, including Gradient Boosting and Random Forests, reflects a deep technical understanding necessary for complex data science projects, particularly in financial fraud detection and risk assessment.
- Cross-functional collaboration and leadership - The CV details Scarlett's ability to work within multi-disciplinary teams to achieve strategic goals. Her experience providing training and guidance to junior analysts signals leadership qualities and her role in inter-departmental collaboration underscores her versatility and ability to drive data-oriented decision-making processes effectively.
How complex should the format of your data scientist CV be?
Perhaps, you decided to use a fancy font and plenty of colours to ensure your data scientist CV stands out amongst the pile of other candidate profiles. Alas - this may confuse recruiters. By keeping your format simple and organising your information coherently, you'll ultimately make a better impression. What matters most is your experience, while your CV format should act as complementary thing by:
- Presenting the information in a reverse chronological order with the most recent of your jobs first. This is done so that your career history stays organised and is aligned to the role;
- Making it easy for recruiters to get in touch with you by including your contact details in the CV header. Regarding the design of your CV header, include plenty of white space and icons to draw attention to your information. If you're applying for roles in the UK, don't include a photo, as this is considered a bad practice;
- Organising your most important CV sections with consistent colours, plenty of white space, and appropriate margins (2.54 cm). Remember that your CV design should always aim at legibility and to spotlight your key information;
- Writing no more than two pages of your relevant experience. For candidates who are just starting out in the field, we recommend to have an one-page CV.
One more thing about your CV format - you may be worried if your double column CV is Applicant Tracker System (ATS) complaint. In our recent study, we discovered that both single and double-column CVs are ATS-friendly . Most ATSes out there can also read all serif and sans serif fonts. We suggest you go with modern, yet simple, fonts (e.g. Rubik, Lato, Raleway) instead of the classic Times New Roman. You'll want your application to stand out, and many candidates still go for the classics. Finally, you'll have to export your CV. If you're wondering if you should select Doc or PDF, we always advise going with PDF. Your CV in PDF will stay intact and opens easily on every OS, including Mac OS.
PRO TIP
Use bold or italics sparingly to draw attention to key points, such as job titles, company names, or significant achievements. Overusing these formatting options can dilute their impact.
The top sections on a data scientist CV
- Technical Skills demonstrate the necessary tools and languages known.
- Professional Experience shows relevant past roles and achievements.
- Education and Certifications highlight formal training and specialisation.
- Projects and Portfolio offer a practical view of skills applied in real-world scenarios.
- Professional Development and Training underscore commitment to ongoing learning.
What recruiters value on your CV:
- Highlight your proficiency with data science tools and programming languages such as Python, R, SQL, and relevant frameworks or libraries; ensure you provide specifics about projects where you've applied these skills.
- Demonstrate your experience with machine learning algorithms and statistical techniques, detailing success metrics from projects where your models significantly improved outcomes.
- Showcase your ability to handle large datasets by mentioning experiences with data warehousing, ETL processes, and your competence in using big data technologies like Hadoop or Spark.
- Include examples of your data visualisation and communication skills, illustrating how you've translated complex results into understandable insights for various stakeholders.
- Exhibit your problem-solving capabilities by outlining scenarios where you've applied data science techniques to address real-world business problems, and the impacts of your solutions.
Recommended reads:
What information should you include in your data scientist CV header?
The CV header is potentially the section that recruiters would refer to the most, as it should include your:
- Contact details - your professional (non-work) email address and phone number;
- Professional photograph - if you're applying hinting at the value you bring as a professional.
Many professionals often struggle with writing their data scientist CV headline. That's why in the next section of this guide, we've curated examples of how you can optimise this space to pass any form of assessment.
Examples of good CV headlines for data scientist:
- Lead Data Scientist | Predictive Analytics & Machine Learning | PhD | Certified Data Professional | 10+ Years
- Senior Data Scientist | AI Deployment & Strategy | Big Data Expertise | MSc Statistics | 7 Years Exp.
- Data Scientist II | Bioinformatics & Genomics | Python & R Proficiency | 5+ Years' Experience
- Junior Data Scientist | Graduate Analyst Program | Data Visualisation & SQL | MEng | Entry-Level
- Data Science Manager | Team Leadership & Project Delivery | Advanced Analytics | PMP | 8 Years' Track Record
- Principal Data Scientist | Finance & Risk Modelling | Deep Learning Specialist | Ph.D. | 12 Years Experience
Catching recruiters' attention with your data scientist CV summary or objective
Located closer to the top of your CV, both the summary and objective are no more than five sentences long and serve as an introduction to your experience. What is more, you could use either to entice recruiters to read on. Select the:
- Summary, if you happen to have plenty of relevant experience. Feature your most impressive accomplishments and up to three skills that are relevant to the job you're applying for;
- Objective, if you're just starting your career off. Provide your career goals and answer how you see the role you are applying for will match your professional growth.
Judging which one you need to add to your data scientist CV may at times seem difficult. That’s why you need to check out how professionals, with similar to your experience, have written their summary or objective, in the examples below:
CV summaries for a data scientist job:
- With over 5 years of experience in leveraging data analytics and machine learning techniques, I have enhanced data-driven decision-making in a fast-paced fintech sector. Skilled in Python, R, SQL, and big data platforms, my crowning achievement includes deploying a predictive model that improved investment strategies by 20%.
- An adept data scientist with 7 years under my belt, I specialise in utilising AI to drive e-commerce growth. Proficient in Python, TensorFlow, and A/B testing, I led a cross-functional team to develop a recommendation engine that boosted average user spend by 30%, significantly outperforming benchmarks.
- Transitioning from a 10-year career in academia as a computational biologist, I am equipped with robust data analysis and statistical modelling expertise. Newly proficient in Python and SQL, I am eager to apply my track record of published peer-reviewed research to solve real-world data problems in a dynamic tech environment.
- After a successful career in financial analysis with expertise in Excel and VBA, I am now seeking to pivot my analytical skills towards data science. Having recently completed a specialised course in Python, data visualisation, and machine learning, I am ready to contribute meaningful insights in a more data-centric industry.
- Eager to embark on a data science journey, my fresh perspective is backed by a strong foundation in mathematics and statistics from my academic career. While I possess no direct experience, my recent certification in data analytics tools like Excel, Python, and Tableau has prepared me to deliver value through insightful data interpretation.
- As a recent graduate with a degree in computer science and a passion for data analytics, I aim to apply the theoretical knowledge and technical acumen gained from university coursework in real-world situations. Despite lacking industry experience, my commitment to mastering Python, R, and machine learning techniques is unwavering.
Best practices for writing your data scientist CV experience section
If your profile matches the job requirements, the CV experience is the section which recruiters will spend the most time studying. Within your experience bullets, include not merely your career history, but, rather, your skills and outcomes from each individual role. Your best experience section should promote your profile by:
- including specific details and hard numbers as proof of your past success;
- listing your experience in the functional-based or hybrid format (by focusing on the skills), if you happen to have less professional, relevant expertise;
- showcasing your growth by organising your roles, starting with the latest and (hopefully) most senior one;
- staring off each experience bullet with a verb, following up with skills that match the job description, and the outcomes of your responsibility.
Add keywords from the job advert in your experience section, like the professional CV examples:
Best practices for your CV's work experience section
- Demonstrate your analytical skills by detailing complex data projects you've led or contributed to, and the impact they had on business decisions or outcomes.
- Quantify your achievements using metrics such as percentages or monetary figures to show the tangible value of your work.
- Showcase your proficiency with data science tools and programming languages like Python, R, SQL, and machine learning libraries by mentioning specific tasks you've completed with them.
- Highlight any experience with big data platforms such as Hadoop or Spark, and your ability to handle large, complex datasets.
- Include examples of data visualisation work and the tools used, such as Tableau or Power BI, to represent your ability to communicate complex information effectively.
- Describe any experience in deploying machine learning models into production and the methodologies used, such as continuous integration or A/B testing.
- Mention your proficiency in statistical analysis and your experience with hypothesis testing, forecasting, or experimental design.
- Advertise your soft skills, particularly in communication and teamwork, by describing how you've collaborated with cross-functional teams to drive data-driven solutions.
- Detail any publications or presentations you've contributed to, particularly those relevant to data science, to establish thought leadership and expertise in the field.
Senior Data Scientist
Principal Data Scientist
DeepMind
05/2018-Ongoing
- Led the machine learning initiatives that increased user engagement by 25% through the development of a personalised content recommendation engine.
- Automated data cleaning processes, which reduced the data preparation time by 40%, allowing more rapid insights generation for stakeholder decision-making.
- Coordinated with cross-functional teams to deliver a predictive maintenance system for manufacturing equipment, which reduced downtime by 15% and saved £200k annually.
Machine Learning Scientist
Data Scientist II
BenevolentAI
02/2014-12/2019
- Designed and deployed an NLP algorithm to analyse customer feedback, achieving a 30% improvement in customer satisfaction scores.
- Spearheaded the data analysis for a market segmentation project, resulting in a targeted marketing strategy that boosted sales by 18% in the first quarter.
- Initiated and led a cross-departmental team to integrate AI-driven forecasting tools into the inventory management system, ultimately reducing stock-outs by 20%.
Data Science Specialist
Lead Data Scientist
AstraZeneca
07/2012-08/2017
- Implemented a fraud detection system using machine learning that decreased fraudulent transactions by 22% within the first six months.
- Conducted extensive A/B testing to optimise website conversion rates, contributing to a significant uplift of 12% in e-commerce sales.
- Orchestrated data-driven optimisation of supply chain logistics, enhancing delivery times by 25% and customer satisfaction by 10%.
Advanced Analytics Developer
Senior Data Scientist
Sky Betting & Gaming
11/2016-05/2021
- Developed an advanced predictive model for customer churn that was instrumental in reducing churn rate by 17% within the customer base.
- Collaborated on a complex data integration project that consolidated disparate data sources into a unified analytics platform, boosting team productivity by 35%.
- Initiated a data literacy program within the company, raising the data-driven decision-making capabilities of non-technical departments.
Business Intelligence Analyst
Data Scientist I
Ocado Technology
09/2009-03/2015
- Designed a suite of interactive dashboards that provided real-time KPIs to executives, leading to a 10% increase in operational efficiency.
- Conducted a complex regression analysis to uncover key drivers of customer loyalty, informing the customer relationship management strategy.
- Participated in the development of a demand forecasting model that accurately predicted seasonal demand fluctuations, improving inventory management by 18%.
Data Analysis Engineer
Data Scientist
Revolut
01/2013-06/2018
- Implemented an anomaly detection system using unsupervised learning algorithms for real-time fraud detection, reducing false positives by 30%.
- Streamlined data warehousing methods that increased data retrieval efficiency by 20%, supporting more agile business responses to market trends.
- Guided a team in the incorporation of advanced prescriptive analytics to refine marketing strategies, achieving a consistent uptick in ROI by 15% year over year.
Statistical Modeling Expert
Data Science Manager
Monzo Bank
04/2015-02/2020
- Developed a Bayesian hierarchical model to improve targeting accuracy of online ad campaigns resulting in a 26% rise in click-through rates.
- Played a pivotal role in the creation of a risk assessment framework using predictive analytics, contributing to a 40% reduction in credit losses.
- Leveraged machine learning to optimise logistics routes, resulting in a 10% reduction in fuel costs and a 5% cut in delivery times.
Predictive Analytics Consultant
Lead Data Scientist
Palantir Technologies
06/2017-Ongoing
- Developed a custom churn prediction model that identified at-risk customers with 85% accuracy, enabling proactive retention strategies.
- Optimised sales forecasting models incorporating seasonality and promotional data, enhancing forecast accuracy to 92%.
- Facilitated workshops on data science best practices for cross-functional teams, significantly enhancing company-wide data utilisation and analytics proficiency.
Writing your CV without professional experience for your first job or when switching industries
There comes a day, when applying for a job, you happen to have no relevant experience, whatsoever. Yet, you're keen on putting your name in the hat. What should you do? Candidates who part-time experience , internships, and volunteer work.
Recommended reads:
PRO TIP
Describe how each job helped you grow or learn something new, showing a continuous development path in your career.
Mix and match hard and soft skills across your data scientist CV
Your skill set play an equally valid role as your experience to your application. That is because recruiters are looking for both:
- hard skills or your aptitude in applying particular technologies
- soft skills or your ability to work in a team using your personal skills, e.g. leadership, time management, etc.
Are you wondering how you should include both hard and soft skills across your data scientist CV? Use the:
- skills section to list between ten and twelve technologies that are part of the job requirement (and that you're capable to use);
- strengths and achievements section to detail how you've used particular hard and soft skills that led to great results for you at work;
- summary or objective to spotlight up to three skills that are crucial for the role and how they've helped you optimise your work processes.
One final note - when writing about the skills you have, make sure to match them exactly as they are written in the job ad. Take this precautionary measure to ensure your CV passes the Applicant Tracker System (ATS) assessment.
Top skills for your data scientist CV:
HARD SKILLS
Statistical analysis
Machine learning
Data mining
Data wrangling
Programming (Python/R)
Database management
Big data technologies
Data visualization tools
Predictive modelling
Deep learning
SOFT SKILLS
Analytical thinking
Problem-solving
Effective communication
Attention to detail
Critical thinking
Teamwork
Time management
Adaptability
Project management
Continuous learning
PRO TIP
If you have received professional endorsements or recommendations for certain skills, especially on platforms like LinkedIn, mention these to add credibility.
Further professional qualifications for your data scientist CV: education and certificates
As you're nearing the end of your data scientist CV, you may wonder what else will be relevant to the role. Recruiters are keen on understanding your academic background, as it teaches you an array of hard and soft skills. Create a dedicated education section that lists your:
- applicable higher education diplomas or ones that are at a postgraduate level;
- diploma, followed up with your higher education institution and start-graduation dates;
- extracurricular activities and honours, only if you deem that recruiters will find them impressive.
Follow a similar logic when presenting your certificates. Always select ones that will support your niche expertise and hint at what it's like to work with you. Balance both technical certification with soft skills courses to answer job requirements and company values. Wondering what the most sought out certificates are for the industry? Look no further:
PRO TIP
If you have received professional endorsements or recommendations for certain skills, especially on platforms like LinkedIn, mention these to add credibility.
Recommended reads:
Key takeaways
Your successful job application depends on how you well you have aligned your data scientist CV to the job description and portrayed your best skills and traits. Make sure to:
- Select your CV format, so that it ensures your experience is easy to read and understand;
- Include your professional contact details and a link to your portfolio, so that recruiters can easily get in touch with you and preview your work;
- Write a CV summary if you happen to have more relevant professional experience. Meanwhile, use the objective to showcase your career dreams and ambitions;
- In your CV experience section bullets, back up your individual skills and responsibilities with tangible achievements;
- Have a healthy balance between hard and soft skills to answer the job requirements and hint at your unique professional value.
