Director, Data Science
New
J
JobgetherTechnology, E-commerce
United StatesFull-TimeDirector
SalaryCompetitive base salary range of $215,000 - $275,000, depending on skills, experience, qualifications, and market factors.
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Job Details
- Experience
- 8+ years of experience in applied data science or machine learning, including at least 3 years managing data science or engineering teams.
- Required Skills
- PythonMachine LearningData scienceMLOps
Requirements
- 8+ years of experience in applied data science or machine learning, including at least 3 years managing data science or engineering teams.
- Experience leading data science initiatives within fast-paced technology, e-commerce, marketplace, retail, or consumer-focused environments.
- Proven expertise in machine learning architectures, including two-tower retrieval systems, transformers, LLMs, contextual bandits, graph neural networks, and causal or uplift modeling.
- Strong experience with search, recommendation, ranking, or personalization systems, including relevance modeling and multi-objective optimization.
- Hands-on programming experience with Python.
- Experience with large-scale ML infrastructure, including feature stores, model registries, real-time serving systems, and experimentation platforms.
- Strong product intuition and business acumen with the ability to connect ML improvements to revenue, customer satisfaction, and operational metrics.
- Demonstrated ability to communicate complex technical concepts clearly to executives and cross-functional stakeholders.
- Track record of building, mentoring, and retaining diverse, high-performing technical teams.
Responsibilities
- Own the end-to-end data science roadmap, aligning machine learning priorities with business goals, measurable outcomes, and strategic initiatives.
- Provide technical leadership, mentorship, and guidance to Data Scientists and ML Engineers while promoting best practices in model development, evaluation, and deployment.
- Partner with senior leadership, Product, Engineering, and Business teams to integrate ML capabilities into product and operational strategies.
- Build a culture of experimentation by defining measurement frameworks, improving A/B testing practices, and connecting model performance to business impact.
- Lead the development of advanced consumer-facing ML solutions, including search ranking, query understanding, personalization, recommendations, and behavioral modeling.
- Drive delivery intelligence initiatives through predictive modeling for ETA optimization, demand forecasting, routing, fleet utilization, and operational efficiency.
- Develop and optimize advertising intelligence solutions, including ranking systems, relevance models, attribution pipelines, bid optimization, and audience targeting.
- Architect scalable machine learning platforms and collaborate with engineering teams to bring models into production environments.
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