Manager, Data Science

Description:

• Lead and mentor a high-performing team of data scientists and support their professional development.
• Own the end-to-end lifecycle of machine learning models from exploration and prototyping through deployment and monitoring.
• Partner with Product, Engineering, and Business teams to turn business problems into data science projects.
• Maintain high standards for code quality, documentation, model validation, and peer review.
• Lead the design and analysis of A/B tests and experimentation frameworks to measure feature impact.
• Work with Data Engineering to ensure data pipelines and infrastructure support rapid model iteration.
• Communicate technical findings to non-technical stakeholders and help translate insights into business decisions.
• Balance hands-on technical work with team leadership as a player-coach.
• Drive execution of the AI roadmap and ensure models deliver measurable business impact.

Requirements:

• 7+ years of experience in Data Science.
• 1–2 years of experience in a formal or informal leadership role, such as Lead Data Scientist or People Manager.
• Deep expertise in machine learning, predictive modeling, and statistical analysis.
• Expert-level proficiency in Python and SQL.
• Experience with ML frameworks such as Scikit-Learn, TensorFlow, or PyTorch.
• Experience working with large-scale datasets and cloud environments such as AWS, GCP, or Azure.
• Experience in B2B, marketplace, travel, or e-commerce industries is highly preferred.
• Proven ability to bridge technical execution and business value.
• Advanced degree in a quantitative field such as Computer Science, Statistics, Math, or Physics is a plus.

Benefits:

• Competitive base salary of $144,500 to $200,000 USD.
• Total compensation may include equity and/or variable pay (OTE).
• Opportunities for bonuses and commissions.
• Hybrid-hub model with options for in-office or fully remote work.
• Perks and benefits available through Engine’s full benefits program.
• Compensation is adjusted based on experience, expertise, and location.

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