Data Scientist Jobs

About The Position

Requirements

• 7+ years of experience in Data Science, Machine Learning, Statistics, or a related field, ideally supporting large-scale online platforms.
• Bachelors degree in Data Science, Computer Science, Statistics, or a relevant field or equivalent industry experience.
• Proven track record of delivering high-quality experimentation analyses and actionable insights that influenced product strategy.
• Deep expertise in statistical testing, causal inference, and experiment design (A/B testing, power analysis, sequential testing, etc.).
• Strong proficiency in Python, SQL, and modern ML/statistics libraries (pandas, scikit-learn, PyTorch/TensorFlow optional).
• Experience working with large-scale datasets in distributed computing environments (Spark, Databricks, Snowflake, etc.).
• Strong familiarity with data engineering best practices and building durable data science assets on top of production-grade data.
• Demonstrated success with dashboarding tools (Tableau, Looker, Mode) and crafting data stories for a variety of stakeholders.
• Experience applying automation and AI-driven tools to streamline workflows, improve standardization, and optimize productivity.
• Exceptional communication skills, especially in translating technical analyses into compelling stories for product managers and executives.
• Ability to lead projects with high ambiguity and cross-functional complexity.

Nice To Haves

• Experience with personalization algorithms, recommender systems, or ranking models.
• Familiarity with streaming or media tech ecosystems.
• Advanced degree in Data Science, Computer Science, Statistics, or a relevant field or equivalent industry experience

Responsibilities

• Provide technical leadership for the data science team, guiding junior and mid‑level data scientists through best practices in experimentation, modeling, analytics, and data storytelling.
• Identify, analyze, and deliver high‑impact insights for ESPN personalization—including user behavior trends, recommendation system performance, and content engagement patterns—to influence product decisions.
• Design and analyze large‑scale A/B and multivariate experiments using rigorous statistical methods, power analysis, variance reduction techniques, and causal inference frameworks.
• Lead the design of automated dashboards, visualizations, and decision‑support tools, while partnering with Data Engineering to build durable, production-ready data pipelines and science workflows.
• Own end-to-end delivery of complex data science initiatives by collaborating with Product, Engineering, Content Programming, and Executive stakeholders, translating technical findings into actionable recommendations.

Benefits

• A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.