Data Manager managing data analytics consulting projects at PwC. Collaborating on data - driven solutions and overseeing implementation while maintaining client relationships.
About the role
- Lead Data Scientist driving innovation in personalization and data insight generation for ESPN's streaming products. Providing technical leadership and overseeing data science projects in a collaborative environment.
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.
Requirements
- 7+ years of experience in Data Science, Machine Learning, Statistics, or a related field
- Bachelor's 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.
Benefits
- A bonus and/or long-term incentive units may be provided as part of the compensation package
- Full range of medical benefits
- Financial benefits
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