ABOUT THE ROLEAdAction is seeking a hands-on Director of Data Science & Engineering to lead our Data function across Machine Learning, Data Science, Analytics, and Data Engineering. You will set the technical direction, develop our people, and strengthen how we use data and scientific thinking to improve products and business outcomes.
Reporting to our VP of Engineering, you will serve as a peer to our Director of Engineering and a member of the Technology leadership team. You will directly manage a small group of Data professionals (ML Engineers, Data Analysts, and Data Engineers).
Our Data professionals remain embedded in cross-functional Mission Aligned Teams (MATs), working alongside Product Managers and Engineers. You will own people leadership, technical standards, and capability development while partnering with MAT leaders on priorities and delivery, rather than creating a separate centralized delivery team.
This is a hands-on leadership role, not solely a strategy or advisory position. You will regularly write code, build and evaluate models, and contribute to production implementations. Your deepest expertise should be in applied ML and Data Science, supported by the Analytics and Data Engineering experience needed to lead the broader function credibly. We make extensive use of AI in our day-to-day work and are looking for an AI-forward leader who actively uses these tools and helps the team adopt them effectively.
Success means a stronger, more independent Data function, more trustworthy analysis and data foundations, and ML initiatives that deliver measurable value.
RESPONSIBILITIES- Directly manage, coach, and develop ML Engineers, Data Analysts, and Data Engineers through clear expectations, actionable feedback, performance management, and discipline-specific growth plans. Shape hiring, develop technical leaders and qualified reviewers, and delegate decisions so quality doesn't depend on your approval of every change.
- Set a practical ML and data strategy with Technology leadership, Product, and business stakeholders, tied to business outcomes and the capabilities we need to build. Evaluate opportunities such as conversion quality, fraud detection, personalization, forecasting, and campaign optimization, establish baselines and success measures, and recognize when better instrumentation, a simple rule, or an analysis beats a model. Track the impact of what we ship and adjust.
- Regularly contribute Python, SQL, models, analytical workflows, and production implementations, using AI-assisted coding and analysis and helping the team adopt these tools without compromising rigor, security, or data privacy. Pair on difficult problems and leave behind tested, documented work others can maintain.
- Raise the standard for scientific thinking through problem framing, testable hypotheses, experiment design, statistical reasoning, model evaluation, and metric design. Coach analysts and Product Managers on measurement plans, communicating uncertainty, distinguishing correlation from causation, and translating evidence into decisions.
- Establish practical standards for data quality, reproducibility, testing, documentation, model deployment, and ongoing evaluation. Ensure important models and datasets have clear monitoring, maintenance, and operational ownership.
- Lead Data Engineering practices across data architecture, modeling, pipelines, and shared datasets, improving data quality, reliability, and accessibility. Partner with our Director of Engineering, Platform team, and MAT Tech Leads on infrastructure, production integration, security, and cost.
- Coordinate cross-team data investments and capacity with MAT leaders, involving Data professionals early in discovery rather than after a solution is chosen. Build reusable capabilities and shared practices that make teams more effective while preserving local ownership.
REQUIRED EXPERIENCE & QUALIFICATIONS- Typically 8+ years of relevant professional experience in Data Science, Machine Learning, or related engineering roles, including substantial hands-on experience delivering applied ML or Data Science solutions and progressively broader technical or team leadership responsibilities.
- Deep applied Machine Learning and Data Science experience, including framing business problems, developing and evaluating models, and taking solutions beyond experimentation into production. Strong judgment around baselines, data leakage, uncertainty, and model limitations.
- Demonstrated leadership of technical practitioners through people management or senior technical leadership, including coaching experienced contributors, providing constructive feedback, setting direction, and improving team outcomes. Readiness and motivation to take direct responsibility for people development, performance management, and team effectiveness.
- Current, hands-on proficiency in Python and SQL, with the ability and desire to regularly contribute maintainable, tested code, models, and analytical workflows.
- Demonstrated use of AI-assisted tools in day-to-day technical work, with the ability to evaluate their outputs, recognize limitations, protect sensitive data, and coach others in their effective use.
- Strong statistical reasoning and analytics experience, including metric design, hypothesis testing, experiment design, and responsible interpretation of results. Ability to explain what the evidence supports and where it remains inconclusive.
- Credible Data Engineering knowledge across data modeling, pipelines, warehouses, and data quality. Ability to coach Data Engineers and evaluate architectural tradeoffs without needing to be the deepest specialist in every technology.
- Experience with the operational lifecycle of data and ML systems, including reproducibility, version control, automated testing, deployment, monitoring, and maintaining solutions after launch.
- Product-oriented judgment and experience prioritizing technical investments against customer and business outcomes. Ability to align multiple teams, navigate dependencies, and balance direct implementation with people and functional leadership.
- Proven ability to teach complex concepts, communicate tradeoffs, and collaborate effectively with technical and nontechnical colleagues in a distributed environment.
We value demonstrated capability, sound judgment, and meaningful results over a particular degree or previous job title. We welcome established Directors, experienced Data Science or Engineering Managers, and Staff- or Principal-level practitioners ready to lead a function. A previous Director title is not required.
PREFERRED EXPERIENCE- Experience in advertising technology, performance marketing, or other transaction- and event-driven products, particularly fraud detection, recommendations, personalization, forecasting, or optimization.
- Experience with data and ML technologies such as Amazon Redshift, dbt, Airflow, MLflow, and Amazon SageMaker, or comparable tools for data transformation, orchestration, experiment tracking, and model deployment and monitoring.
- Experience leading multiple data disciplines in a smaller organization or supporting specialists embedded in cross-functional product teams.
- Experience with real-time or streaming analytics and event-driven data pipelines, including tradeoffs around latency, data freshness, correctness, and operational complexity.
- Experience designing or evolving data architectures that support analytics and ML, including storage and processing patterns, data modeling, and shared data foundations.
- Prior direct people-management experience, including career development, performance reviews, hiring, and addressing performance challenges.
CULTURE FIT- Curious and evidence-driven: ask thoughtful questions, challenge assumptions constructively, and change your mind when the evidence changes.
- AI-forward mindset: actively explore and adopt AI tools, share what works, and continually improve how you and your team work while retaining accountability for quality and outcomes.
- Hands-on ownership: enjoy building useful solutions and developing the people who build them, rather than leading only through reviews and presentations.
- Pragmatic scientific rigor: choose methods appropriate to the decision, favor simple solutions when they work, and balance learning with timely delivery.
- Collaborative leadership: work comfortably within a Mission Aligned Team framework, build alignment across disciplines, and strengthen others without taking over their work.
- Continuous improvement: value pairing, small increments of delivery, automation, testing, observability, and shared operational ownership.
- Teaching and direct feedback: explain your reasoning clearly, invite different perspectives, and help the team become increasingly capable and independent.
