Director of Data Science – Engineering

This is a fully remote position, open to applicants in Colorado, +2 more states.

📋 Description

• Oversee AdAction’s Data function encompassing Machine Learning, Data Science, Analytics, and Data Engineering.

• Define the technical direction and nurture the development of the Data organization.

• Directly supervise, mentor, and cultivate the skills of ML Engineers, Data Analysts, and Data Engineers.

• Influence hiring processes, develop technical leaders and qualified reviewers, and delegate decision-making responsibilities.

• Collaborate with Technology leadership, Product, and business stakeholders to establish ML and data strategy.

• Assess opportunities related to conversion quality, fraud detection, personalization, forecasting, and campaign optimization.

• Set baselines and success metrics, monitor impact, and modify initiatives accordingly.

• Utilize Python and SQL to write code, build and assess models, and contribute to production implementations.

• Employ AI-assisted coding and analysis, ensuring rigor, security, and adherence to data privacy.

• Collaborate on challenging problems and deliver tested, well-documented work.

• Enhance scientific reasoning through problem framing, hypothesis formulation, experimental design, statistical analysis, model evaluation, and metric development.

• Guide analysts and Product Managers in crafting measurement plans and making evidence-based decisions.

• Set standards for data quality, reproducibility, testing, documentation, deployment, and ongoing assessment.

• Guarantee that models and datasets receive monitoring, maintenance, and operational oversight.

• Lead initiatives in data architecture, modeling, pipelines, and shared datasets.

• Collaborate with Engineering, Platform, and Mission Aligned Team leads on infrastructure, production integration, security, and cost management.

• Coordinate investments in data across teams and manage capacity effectively.

• Develop reusable capabilities and shared practices while maintaining local ownership.

⛳️ Requirements

• Typically possess 8+ years of relevant professional experience in Data Science, Machine Learning, or related engineering fields.

• Extensive hands-on experience in delivering applied ML or Data Science solutions.

• Increased technical or team leadership responsibilities over time.

• Profound experience in applied Machine Learning and Data Science.

• Proven leadership of technical professionals through people management or senior technical roles.

• Current, hands-on expertise in Python and SQL.

• Demonstrated use of AI-assisted tools in everyday technical tasks.

• Strong foundation in statistical reasoning and analytics, including metric design, hypothesis testing, experimental design, and responsible result interpretation.

• Credible knowledge in Data Engineering covering data modeling, pipelines, warehouses, and data quality.

• Experience with reproducibility, version control, automated testing, deployment, monitoring, and maintaining data and ML solutions post-launch.

• Product-oriented judgment with experience prioritizing technical investments based on customer and business outcomes.

• Capability to align diverse teams, navigate dependencies, and balance implementation with leadership in people and functions.

• Proven ability to teach complex concepts and collaborate effectively with both technical and non-technical colleagues in a distributed setting.

• Preferred: experience in advertising technology, performance marketing, fraud detection, recommendations, personalization, forecasting, or optimization.

• Preferred: familiarity with Amazon Redshift, dbt, Airflow, MLflow, Amazon SageMaker, or similar tools.

• Preferred: experience leading multiple data disciplines within a smaller organization or supporting specialists in cross-functional product teams.

• Preferred: experience with real-time or streaming analytics and event-driven data pipelines.

• Preferred: experience designing or evolving data architectures that support analytics and ML.

• Preferred: prior direct experience in people management.

🏝️ Benefits

• Fully remote work arrangement.

• Autonomous work environment.

• Opportunities for both personal and professional growth.

• Inclusive and collaborative culture.