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About This Role
### About the Role:
The Marketing Data Scientist is a rigorous, intellectually curious problem solver embedded directly within the Marketing function for the GTM team. They own the science behind how we understand, attract, and grow our customers — building the models, measurement frameworks, and experiments that turn complex marketing data into decisions that move the business forward. They do not just report on what happened — they design the studies that tell us why and build the models that tell us what comes next.
### What You'll Do:
- Frame ambiguous marketing problems as well\-scoped modeling and measurement challenges and own them end to end.
- Develop and own attribution models that accurately allocate marketing investment across channels and touchpoints.
- Build propensity, lead scoring, and churn models to prioritize where GTM teams should focus.
- Model the relationship between product engagement signals and downstream commercial outcomes — expansion, retention, and conversion.
- Design, execute, and interpret experiments (A/B, MVT, geo\-based) with appropriate power analysis and statistical validity.
- Build and apply segmentation models and cohort analyses to uncover behavioral patterns, lifecycle trends, and funnel opportunities.
- Analyze organic search and AEO signals as modeling inputs to inform content strategy and improve discoverability.
- Partner with analytics engineers to productionize models and move insights from notebook to pipeline.
- Use AI tools to move faster, explore unfamiliar methods, and surface modeling options — while developing the judgment to know when AI\-generated outputs are wrong or incomplete.
### What You'll Bring:
- Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field; Master's or PhD is a bonus.
- 4 to 6 years applying data science in a product, growth, or marketing context at a high\-growth company.
- Strong command of Python (pandas, scikit\-learn, statsmodels, or similar) and analytical SQL.
- Demonstrated experience building predictive models that influenced real business decisions.
- Hands\-on experience with marketing measurement — attribution, media mix modeling, incrementality testing, or similar.
- Solid statistical foundation — forecasting, regression, classification, causal inference, and experiment design.
- Experience with GA4, Google Ads, and digital marketing measurement platforms.
- Strong understanding of reverse ETL processes and operationalizing model outputs.
- Strong storytelling skills — able to translate statistical complexity into clear, actionable business language for both technical and non\-technical audiences.
### Bonus/Nice to Have:
- Experience with MLOps practices and moving models from experimentation to production.
- Comfort working across the full stack — dbt, Hex, Snowflake, Looker, Mode, Segment, and similar tools.
- Familiarity with SaaS, developer tools, or B2B product\-led growth metrics.
- Hands\-on experience with AI tools like Claude, Cursor, or similar LLM\-powered assistants to accelerate analytical workflows.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
About CircleCI
CircleCI is the world's largest continuous integration/continuous delivery (CI/CD) platform, and the hub where code moves from idea to delivery. As one of the most\-used DevOps tools \- processing more than 3 million jobs a day \- CircleCI has unique access to data on how the most effective engineering teams work, and the tools to help software companies successfully leverage the power of AI into their commercial applications. Companies like Hinge, HuggingFace, and Samsung use us to improve engineering team productivity, release better products, and get to market faster.
Founded in 2011 and headquartered in downtown San Francisco with a global, remote workforce, CircleCI is venture\-backed by Base10, Greenspring Associates, Eleven Prime, IVP, Sapphire Ventures, Top Tier Capital Partners, Baseline Ventures, Threshold
Ventures, Scale Venture Partners, Owl Rock Capital, Next Equity Partners, Heavybit, and Harrison Metal Capital.
CircleCI is an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.
Salary Context
This $145K-$196K range is above the median for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At CircleCI, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($170K) sits 14% below the category median. Disclosed range: $145K to $196K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
CircleCI AI Hiring
CircleCI has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span San Francisco, CA, US, Remote, US. Compensation range: $196K - $235K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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