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About This Role
### About the Role:
The Manager of Data Science for GTM is a hands\-on player coach who leads from the front across Sales, Customer Success, and Marketing analytics. They are deeply technical, opinionated about the GTM analytical agenda, and accountable for both the output of the team and the development of each person on it. They are not a pure people manager — they roll up their sleeves on the hardest problems, set the standard for what good looks like, and pull the team up through hands\-on coaching, technical mentorship, and direct involvement in the work. They own the analytical strategy for the GTM function and are a credible, trusted partner to Sales, CS, and Marketing leadership.
### What You'll Do:
- Set the analytical strategy and roadmap for GTM, proactively identifying the highest\-leverage opportunities before they are asked, across the full customer lifecycle from acquisition through expansion and retention.
- Operate as a player coach, stay technical, get into the weeds on the hardest GTM problems, and model the analytical rigor and DS thinking you expect from the team.
- Coach the team directly on the work itself: review analyses, push on methodology, challenge correlation versus causation, and raise the bar for statistical depth, EDA, and storytelling.
- Own the measurement strategy for GTM, defining how we evaluate the true impact of marketing campaigns, sales motions, and CS interventions through rigorous causal methods, not just attribution heuristics.
- Drive development and iteration of churn risk, health scoring, and expansion propensity models, ensuring they are statistically sound and actively used by CS and Sales to prioritize accounts.
- Lead marketing attribution methodology, moving the team beyond last\-touch toward multi\-touch and incrementality\-based approaches that give leadership an accurate picture of what is actually driving pipeline.
- Partner with Revenue Operations and Finance on pipeline and revenue forecasting models that inform quota setting, capacity planning, and board\-level reporting.
- Partner with Sales, CS, and Marketing leadership as a strategic thought partner. Influence decisions on territory design, segmentation, campaign investment, and retention strategy before they are made.
- Grow each person on the team intentionally, set clear development goals, give direct feedback often, and hold the bar high on both performance and growth.
- Champion AI as a force multiplier. Build agents and skills that transition repetitive GTM data work into automated capabilities and free the team to focus on higher\-leverage problems.
### What You'll Bring:
- 7\+ years of experience in data science or quantitative research, with at least 2 years managing a team of data scientists.
- Strong hands\-on technical skills: SQL, Python, and modern data warehouse environments. You can still write the code and review the work.
- Demonstrated experience applying analytical methods to GTM problems: churn modeling, propensity scoring, attribution, forecasting, or similar.
- Experience with causal inference methods (DiD, synthetic control, regression discontinuity, or similar) applied to real GTM or business problems.
- Deep experience with experimental design, A/B testing, and rigorous measurement frameworks in a marketing or sales context.
- Strong track record of influencing GTM strategy through data\-driven recommendations at the leadership level.
- Exceptional storytelling skills, able to translate complex methodology and findings into clear, compelling narratives for Sales, CS, and Marketing leaders.
- Track record of developing data scientists and helping them grow from reactive analytics to proactive, strategic insight generation.
### Bonus/Nice To Have:
- Hands\-on experience with churn risk modeling, health scoring, or customer lifetime value models in a B2B SaaS context.
- Experience with marketing attribution beyond last\-touch, media mix modeling, incrementality testing, or multi\-touch attribution.
- Familiarity with revenue forecasting and pipeline modeling in partnership with RevOps or Finance.
- Background in SaaS, developer tools, or B2B product\-led growth metrics.
- Experience with CRM data (Salesforce or HubSpot) and marketing automation platforms as analytical data sources.
- Hands\-on experience with AI tools like Claude, Cursor, or similar LLM\-powered assistants and a track record of helping a team adopt them.
- Experience with probabilistic modeling or Bayesian approaches to forecasting and scoring.
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 $188K-$235K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At CircleCI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($211K) sits 17% above the category median. Disclosed range: $188K to $235K.
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.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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.
Frequently Asked Questions
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