Senior ML & Data Science Leader - Scale Personalization

National Geographic

Boston (MA)

On-site

USD 180,000 - 260,000

Full time

7 days ago
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Job summary

DraftKings Inc. in Boston seeks an experienced Head of Data Science to lead a high‑performing team of data scientists and ML engineers.

You will set the strategy, roadmap, and priorities for personalization initiatives at scale, translating business goals into high‑impact data science programs. You will guide the development and deployment of ML systems across the full lifecycle, partnering with Product, Engineering, Marketing, Revenue, and Data Science to connect technical decisions to business

Qualifications

  • Bachelor’s degree in CS, data science, ML, or related field; advanced degree preferred.
  • At least 10 years in data science or ML engineering with ≥5 years people leadership.
  • Experience leading teams from technical direction to production deployment and optimization.

Responsibilities

  • Lead, coach, and develop a high-performing data team.
  • Set strategy, roadmap, and priorities for initiatives.
  • Partner with senior leaders to align tech decisions with business outcomes.
  • Guide ML systems across full model lifecycle.
  • Influence architecture of model-driven experiences and data pipelines.
  • Advance MLOps, automation, testing, and production deployment.
  • Own capacity planning and allocate resources.

Skills

People leadership
Mentoring
Strategic thinking
Communication
Cross-functional collaboration
Model lifecycle

Education

Bachelor's degree in Computer Science / Data Science
Advanced degree preferred

Tools

Python
SQL
Cloud platforms (AWS/GCP)
Data platforms (Spark, Snowflake)

Job description

DraftKings Inc. in Boston seeks an experienced Head of Data Science to lead a high‑performing team of data scientists and ML engineers.

You will set the strategy, roadmap, and priorities for personalization initiatives at scale, translating business goals into high‑impact data science programs. You will guide the development and deployment of ML systems across the full lifecycle, partnering with Product, Engineering, Marketing, Revenue, and Data Science to connect technical decisions to business

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