Lead Data Scientist

We're hiring a senior data scientist to own applied data science for Twist and our Analytics Engine - modeling what makes content perform, how audiences convert from discovery to premium viewing, and how early signals predict which pilots are worth greenlighting. This is a hands-on modeling and analysis role where you will work alongside our Junior Data Analyst (pairing and mentoring), partner with our senior data science advisor and operate within the analytical roadmap set by the Director of Research. What you'll own • Applied modeling for the Analytics Engine - content performance scoring, engagement classification, sentiment analysis, and campaign attribution. • The pilot performance prediction loop - forecasts vs. actual outcomes, calibration, and accuracy tracking. • Modeling of Twist user behavior post-launch - acquisition attribution, watch-depth, retention, and conversion from campaign touch to in-app engagement. • Audience segmentation with real analytical depth, not surface-level demographics. • Mentorship of our data analyst, and the team's overall statistical rigor. • Turning findings into recommendations Producers and leadership can actually act on. Must-haves • Skeptical of metrics that look good but don't actually predict outcomes. • 6+ years of hands-on data science work with real, measurable business impact. • Strong Python and SQL, with a solid grounding in statistics, experimental design, and ML fundamentals. • NLP experience - sentiment analysis, text classification, and comfort working with LLM-based pipelines. • Attribution modeling experience, ideally where attribution is genuinely hard (short-form video, organic social, multi-touch). • Able to take an analytical agenda and run with it without daily direction. • Can explain findings clearly to people who aren't data scientists. Nice to Have • Media, streaming, entertainment, or creator economy experience. • Recommendation systems or content discovery ML background. • Experience with modern data stacks (Snowflake, dbt, lakehouse architectures). • Experience with LLM evaluation, prompt versioning, or agentic workflow patterns.