Sigma

Built in partnership with Owl & Co (Hernan Lopez, ex-Wondery/Fox/NatGeo) to determine what a streaming subscriber is worth once you combine subscription payments with ad revenue monetization. The model covers every major US streaming service (Netflix, Disney+, HBO Max, Hulu, ESPN, Peacock, Paramount+, and more), at the grain of individual plan, distributor, and billing cadence. Tech stack: Snowflake (warehouse, SQL transform layer), Sigma (semantic layer, editable write-back input tables, dashboards), Claude Code and Codex / MCP tooling (AI-assisted data modeling, SQL, and Sigma workbook builds), HTML/JS for client-facing dashboard prototyping. ...

Ported a Claude Code-developed, synthetic Netflix streaming analytics HTML dashboard (4 tabs, ~10K subscribers, 105K watch sessions) to a production Sigma workbook, rebuilding the Python data pipeline from scratch in order to generate Sigma-compatible CSVs that were added to and power a Sigma data model. Key technical work: Rewrote sigma_export.py, expanding output from four CSVs to nine; added waterfall.csv, top_titles.csv, top_titles_by_plan.csv, device_summary.csv, and monthly_views.csv as pre-aggregated files for charts that could not be derived cleanly in Sigma from grain-level session data Applied all notebook transformations at row level: device watch/completion multipliers, seasonality on watch duration, plan duration/completion multipliers; device session count overrides and monthly seasonality on counts/rates handled in pre-aggregated files Added subscription_plan dimension to cohort_retention.csv and waterfall.csv so that the plan filter control on the LTV tab correctly targets all three data sources Completed in Sigma: LTV tab (all 4 charts + KPIs with working plan filter) and Content tab (all 5 KPIs + 4 charts across 4 data sources)

Led a 0-to-1 subscription, advertising, and engagement demand forecasting data product. When you’re streaming 50k+ events per year, you cannot possibly forecast demand without an automated, scalable data infrastructure and continually improving ML-based forecasting models. You also should be able to dynamically compare alternative portfolio scenarios, such as decisions to add a new rights contract, to optimize revenue and profitability. In our second iteration after our initial Streamlit application, I led the development of a Sigma-based AI and data application that projects signups, subscribers, subscription and ad revenue, and engagement at the contract level across all of our sports verticals. The underlying Python forecasting engine uses a cohort-based retention framework, historical plan mix, and viewership and live minutes 1P data to allocate revenue across signup, engagement, inactive, and dormant components. Ad revenue layers in based on forecasted engagement and inventory monetization, producing contract-level base forecasts that can also be compared dynamically against alternative portfolio scenarios. ...