Lead Data Scientist, Philo

• 10+ years of professional experience in data science/analytics with demonstrated proficiency in Python, SQL, or R and statistical modeling
• 3+ years of experience leading data science teams and managing complex statistical projects
• Deep expertise in statistical methods and causal inference
• Strong experience in experimental design and A/B testing
• Deep experience in building a robust and scalable data model as the foundation for both analysis and advanced modeling
• Proven track record of delivering high-impact data science projects that scale
• Experience mentoring junior data scientists and raising team capabilities
• Excellent communication skills with ability to translate complex findings into business insights through effective data visualization and dashboards
• Strong business acumen and ability to connect analytics to business outcomes
• Experience with modern data science tools and technologies, including Python, Cursor, dbt, GrowthBook, and BI dashboarding tools

Desirable

• Data underpins everything we do at Philo: making informed business decisions; analyzing and improving the quality of our streaming experience; running product experiments to optimize our signup flows and improve user journeys; and making it effortless for our users to find the perfect thing to watch
• Philo serves over a billion streams to its users every year, generating a wealth of data that we leverage at all levels of the organization
• Philo’s data pipeline processes nearly 8 trillion events per year into our petabyte scale data lake, where we run over 30k ETL and BI queries every day to bring data-driven insights to our team
• On the Data Science team, we’re looking for people who excel at deep statistical and data modeling, experimentation, and identifying actionable insights on a variety of interesting engagement, content discoverability, acquisition, and retention-related problems across our streaming service
• You’ll be working closely with other data scientists, analysts, and engineers to build and deploy solutions directly for our service
• In addition, you’ll work with stakeholders in other departments to understand our business needs and deliver business-focused data insights to help the entire team thrive
• We are passionate about problem-solving and providing data-informed insights through rigorous statistical methods, using both cutting-edge techniques and proven practices in close collaboration with every department
• To complete our work we build on modern open-source tools including dbt, Cursor, GrowthBook, Robyn, Superset, and PyTorch, as well as SaaS tools such as Segment, Redshift, SageMaker, AWS Glue, Mode, Avo, and BigEye
• Building multi-touch attribution models to optimize customer lifetime value and calculate RoAS (Return on Ad Spend)
• Modeling to determine which early product behaviors predict conversion and retention
• Running experiments to evaluate and improve our ML-based content recommendation/personalization system
• Building data models for analysis of lifecycle marketing communications impact
• Modeling to analyze the impact of Quality of Experience events (e.g. rebuffering) on customer engagement and other business metrics
• Price sensitivity research and competitive market analyses
• Philo is looking for a Lead Data Scientist to join our team and help elevate our data science practice
• This role will provide senior leadership, establish methodological rigor, and coach our data science team to deliver high-impact analytical solutions
• You have strong opinions about what high-quality Data Science work looks like and are impact oriented
• You can work across multiple business units and have a keen sense of how to connect Data Science outputs to organizational KPIs
• Set the roadmap and model best practices for data science work at Philo
• Lead complex analytical projects that drive significant business value collaborating with stakeholders in the C-Suite
• Establish and evangelize methodological standards for data science work
• Coach and mentor data scientists to elevate the team’s technical capabilities
• Collaborate with product, engineering, and business teams to identify new opportunities
• Develop frameworks for measuring impact of data science initiatives
• Build a culture of experimentation and continuous improvement
• Represent the data science team in cross-functional leadership discussions