Senior Level Data Science Analyst Jobs
Senior level data science analyst jobs put experienced professionals in charge of analytical strategy, modeling pipelines, and the cross-functional teams or projects that translate data into decisions. Hiring concentrates across Technology & Software, Education, and Banking & Financial Services, with employers like Capital One, Lyft, and Plaid hiring at this level now.
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Showing 5 of 56+ Senior Level Data Science Analyst jobs
INTRODUCTION
GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.
ABOUT THE ROLE
As Senior Manager, Data Science, you'll lead Product Data Science: a team of 6 Data Scientists within GitLab's data organization, reporting to the Director of Data Science. You have two mandates:
- Drive product impact now. Set the analytical agenda for your product areas and make sure the team's work changes decisions.
- Build the merged Data Science function. Define excellence for a Data Scientist who blends analytics and data science, and grow the team to that bar. You lead mainly through others, and step in hands-on when it matters: reviewing a model, pressure-testing an analysis, or writing the query yourself.
WHAT YOU’LL DO
- Set a long-range vision and roadmap for Product Data Science, and show the team how its goals connect to company strategy
- Own prioritization and trade-offs across product areas, and adapt the team as priorities shift
- Be the go-to partner for Product and Engineering leaders on complex and urgent questions, choosing the right approach: a deep dive, an experiment, or a production model
- Raise risks and resource gaps early, and bring senior leaders clear options to drive timely decisions
- Hire, coach, and retain a high-performing team: build career paths, act on team feedback, and make tough talent decisions when needed
- Build a high-trust culture of urgency and accountability, where the team communicates openly, shares progress on a predictable cadence, and delivers quality results on time
- Set standards for experimentation, causal inference, metric definition, and production modeling
WHAT YOU’LL BRING
- 10+ years in data science, product analytics, or a related field, including 3+ years managing data science or analytics teams in B2B SaaS or tech
- Strong product sense and a track record as a trusted partner to Product and Engineering leaders
- Exceptional communicator who makes complex problems and findings clear to any audience, from Product leaders to executives, and coaches others to do the same
- Thrives in ambiguity: structures undefined problems, gathers the right information, and makes sound decisions on high-risk, cross-team issues
- Courage to challenge: offers dissenting views, asks tough questions, and fosters inclusive debate
- Deep expertise in statistics and experimentation, including hands-on use of quasi-experimental methods (difference-in-differences, synthetic control, propensity score matching)
- Expert SQL (Snowflake preferred) and strong Python, with experience taking ML models to production
- Fluent with AI tools, with sound judgment on when AI output can be trusted
- Able to use GitLab, thrive in an all-remote, asynchronous company, model GitLab's remote working competencies, and work in line with GitLab's values
NICE TO HAVE
- Experience with developer tools, DevSecOps, or product-led growth products
- Experience measuring adoption and quality of AI-powered product features
- Thought leadership in data science or analytics, internally and externally
- Advanced degree in a quantitative field
ABOUT THE TEAM
Product Data Science brings Data Science and Analytics together into one function. Our Data Scientists are business partners first, pairing strong product judgment with deep skill in SQL, Python, statistics, and machine learning. We help Product and Engineering decide what to build, measure what worked, and understand how customers get value from GitLab. The opportunity is growing fast. AI is driving growth in GitLab's consumption business, creating new ways for data to drive growth. As AI takes over routine reporting, the team focuses on the hard problems: experimentation, causal inference, production models, and recommendations leaders act on.
COMPENSATION
The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary.
- United States Salary Range: $152,800—$259,200 USD
HOW GITLAB SUPPORTS FULL-TIME EMPLOYEES
- Benefits to support your health, finances, and well-being
- Flexible Paid Time Off
- Team Member Resource Groups
- Equity Compensation & Employee Stock Purchase Plan
- Growth and Development Fund
- Parental Leave
Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.
COUNTRY HIRING GUIDELINES:
GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.
PRIVACY POLICY:
Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know during the recruiting process.
INTRODUCTION
GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.
ABOUT THE ROLE
As Senior Manager, Data Science, you'll lead Product Data Science: a team of 6 Data Scientists within GitLab's data organization, reporting to the Director of Data Science. You have two mandates:
- Drive product impact now. Set the analytical agenda for your product areas and make sure the team's work changes decisions.
- Build the merged Data Science function. Define excellence for a Data Scientist who blends analytics and data science, and grow the team to that bar. You lead mainly through others, and step in hands-on when it matters: reviewing a model, pressure-testing an analysis, or writing the query yourself.
WHAT YOU’LL DO
- Set a long-range vision and roadmap for Product Data Science, and show the team how its goals connect to company strategy
- Own prioritization and trade-offs across product areas, and adapt the team as priorities shift
- Be the go-to partner for Product and Engineering leaders on complex and urgent questions, choosing the right approach: a deep dive, an experiment, or a production model
- Raise risks and resource gaps early, and bring senior leaders clear options to drive timely decisions
- Hire, coach, and retain a high-performing team: build career paths, act on team feedback, and make tough talent decisions when needed
- Build a high-trust culture of urgency and accountability, where the team communicates openly, shares progress on a predictable cadence, and delivers quality results on time
- Set standards for experimentation, causal inference, metric definition, and production modeling
WHAT YOU’LL BRING
- 10+ years in data science, product analytics, or a related field, including 3+ years managing data science or analytics teams in B2B SaaS or tech
- Strong product sense and a track record as a trusted partner to Product and Engineering leaders
- Exceptional communicator who makes complex problems and findings clear to any audience, from Product leaders to executives, and coaches others to do the same
- Thrives in ambiguity: structures undefined problems, gathers the right information, and makes sound decisions on high-risk, cross-team issues
- Courage to challenge: offers dissenting views, asks tough questions, and fosters inclusive debate
- Deep expertise in statistics and experimentation, including hands-on use of quasi-experimental methods (difference-in-differences, synthetic control, propensity score matching)
- Expert SQL (Snowflake preferred) and strong Python, with experience taking ML models to production
- Fluent with AI tools, with sound judgment on when AI output can be trusted
- Able to use GitLab, thrive in an all-remote, asynchronous company, model GitLab's remote working competencies, and work in line with GitLab's values
NICE TO HAVE
- Experience with developer tools, DevSecOps, or product-led growth products
- Experience measuring adoption and quality of AI-powered product features
- Thought leadership in data science or analytics, internally and externally
- Advanced degree in a quantitative field
ABOUT THE TEAM
Product Data Science brings Data Science and Analytics together into one function. Our Data Scientists are business partners first, pairing strong product judgment with deep skill in SQL, Python, statistics, and machine learning. We help Product and Engineering decide what to build, measure what worked, and understand how customers get value from GitLab. The opportunity is growing fast. AI is driving growth in GitLab's consumption business, creating new ways for data to drive growth. As AI takes over routine reporting, the team focuses on the hard problems: experimentation, causal inference, production models, and recommendations leaders act on.
COMPENSATION
The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary.
- United States Salary Range: $152,800—$259,200 USD
HOW GITLAB SUPPORTS FULL-TIME EMPLOYEES
- Benefits to support your health, finances, and well-being
- Flexible Paid Time Off
- Team Member Resource Groups
- Equity Compensation & Employee Stock Purchase Plan
- Growth and Development Fund
- Parental Leave
Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.
COUNTRY HIRING GUIDELINES:
GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.
PRIVACY POLICY:
Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know during the recruiting process.
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Who's Hiring
Top Industries Hiring
- Technology & Software14
- Education8
- Banking & Financial Services5
- Biotechnology & Pharmaceuticals5
- Insurance4
Senior Level Data Science Analyst Jobs: Frequently Asked Questions
How do I get a senior level data science analyst job?
Employers hiring at this level want candidates who have owned analytical projects end to end, not just contributed to them. Demonstrating that you have shaped methodology, presented findings to executive stakeholders, and mentored junior analysts gives you a clear edge. A portfolio showing business impact, not just technical output, is what moves senior candidates to the top of a shortlist.
Which companies hire senior level data science analysts?
Companies hiring senior level data science analysts right now include Capital One, Lyft, and Plaid, based on current listings on Migrate Mate as of November 2026. Hiring at this level tends to come from large enterprises and fast-scaling organizations that need analysts who can lead work independently rather than operate under close supervision.
Are there remote senior level data science analyst jobs?
Yes, remote and hybrid options are widely available at this level. About 41% of senior level data science analyst openings are remote or hybrid as of November 2026, reflecting strong employer demand for experienced candidates regardless of location. Senior roles are among the most flexible because the expectation is autonomous delivery, which translates well to distributed work.
What makes a data science analyst role senior level?
Senior level roles are defined by scope and ownership. Where a mid-level analyst executes defined tasks, a senior analyst owns the problem framing, selects the methodology, and is accountable for the outcome. Expectations typically include leading workstreams, collaborating directly with business leadership, and mentoring analysts earlier in their careers. Technical depth alone is not enough without the ability to drive decisions.
Which industries hire the most senior level data science analysts?
Senior level data science analyst roles concentrate in Technology & Software, Education, and Banking & Financial Services, based on current listings on Migrate Mate as of November 2026. These sectors drive hiring at this level because they generate large, complex datasets and need analysts who can independently structure analytical programs rather than support existing ones.