Sr Data Science Engineer, Discovery

The trajectory from data scientist to ML engineer on this team is explicit, not aspirational.

As the analytical foundation matures, the work shifts:

• Feature engineering for recommendations. Evaluate which new signals (voting history, explicit ratings, content metadata, theatrical engagement) improve recipe performance in AWS Personalize. Graduate from analyzing features to building them.
• Model prototyping and evaluation. Prototype recommendation approaches (content-based filtering, hybrid models, embeddings) and evaluate them against the golden eval set you built in your first months.
• Owning a model from experimentation to deployment. When the team outgrows Personalize, you’ll take a model from notebook to production: writing testable Python, managing data lifecycles (pipelines, feature stores, monitoring, retraining), and thinking about systems design (latency, failure modes, observability).

The timing of this transition depends on the work, not a calendar.

You won’t be pushed into model building before the foundation is solid, and you won’t be held back once it is.

• Statistical rigor. You design experiments correctly: power analysis, multiple comparisons, confidence intervals, Bayesian methods where appropriate. You can explain to a non-technical stakeholder why a result is or isn’t significant.
• Causal inference chops. You’ve worked with observational data where naive correlations are misleading. Familiar with propensity score matching, difference-in-differences, instrumental variables, or regression discontinuity. You know when to reach for them.
• SQL and Python fluency. SQL is your first language for data exploration. Python for analysis, modeling, and automation. Your code is clean enough that someone else can read it six months later.
• Experimentation design and analysis. You’ve designed, run, and analyzed A/B tests in production. You understand interaction effects, novelty effects, and Simpson’s paradox.
• Communication. You translate complex analysis into clear narratives. Stakeholders trust your conclusions because you show your reasoning, name your assumptions, and flag what you don’t know.
• Data modeling.

with dbt or equivalent transformation frameworks.

You’ve built analytical data models that other teams actually use.

• Signals you’re on the MLE trajectory Not requirements for day one, but what tells us you’ll grow into model ownership:
• You write Python like a software engineer, not just a notebook user: tests, packaging, code reviews.
• You’ve thought about what happens after an analysis becomes a model: data pipelines, feature generation, monitoring, retraining.
• You’re curious about systems design for ML features: latency, throughput, failure modes, observability.
• You’ve touched some part of the lifecycle around a deployed model, even if it wasn’t your primary job.

• 6+ years as a data scientist or senior analytical role.
• Experience with large-scale user engagement and behavior data. Streaming, entertainment, marketplace, or consumer subscription domains preferred.
• Track record of defining metrics frameworks that stakeholders actually adopted.
• Familiarity with modern data tools: dbt, data warehousing (Snowflake, BigQuery, Redshift), experimentation platforms (GrowthBook, Optimizely), BI tools (Rill, Looker).
• Experience with recommendation systems or personalization is a strong plus, not a prerequisite.
• Familiarty with AI

• A catalog of roughly 1,100 titles that has grown 10x in two years, with heavy top-title concentration. The top 10% of titles drive the majority of watch hours. Discovery needs to surface the long tail.
• Three platforms (TV, mobile, web) with starkly different engagement patterns. TV drives the highest engagement but is mostly single-title sessions. Mobile has more browsing behavior. Web is underserved.
• A recommendation system (AWS Personalize) that shows strong retention signal for engaged users but has significant precision and coverage gaps to close.
• A unique data asset in Guild voting behavior. Members vote on what gets produced and funded before they ever watch it. That signal may be the most differentiated input the recommendation system has.
• A content model unlike general streaming: faith-friendly, owned IP, theatrical-to-streaming pipeline. What “good discovery” means here is genuinely different from Netflix or Spotify.

In the first six months, the Discovery team has a metrics framework they trust and use weekly, the experimentation pipeline is running and producing confident results, and you’ve surfaced at least one behavioral insight that changed how the team prioritized its roadmap.

By year one, you’re contributing to feature engineering for recommendations, you’ve prototyped and evaluated at least one model improvement, and the team has a data-informed view of when Personalize is sufficient and when custom models are warranted.

Work environment: Hybrid team members must have a private and quiet area for working hours in their location.

When in the main office, expect a comfortable, air-conditioned work environment.

Team members are issued their own desks, but the office is an open, shared space and can be fast-paced and occasionally noisy.

Physical demands: Will need to be able to sit or stand at a desk for extended periods of time.

Position type and expected hours of work: Regular full-time, 40 hours per week.

Travel required: 2-4 onsite events in Utah each year.

There may be other opportunities to travel, but no other significant out-of-state travel is anticipated

Work authorization: Must be authorized to work in the United States.

EEO statement: At Angel Studios, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants and teammates.

Other duties: Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the team member for this job.

Duties, responsibilities and activities may change at any time with or without notice.