Build and own the platform's first recommendation features using collaborative filtering, embeddings, similarity search, and ranking. Handle sparse data and cold-starts, evaluate with offline metrics and online experiments, and partner with backend engineering to serve models and set ML practices.
Termgrid is rewriting the rules of private capital markets. We built the category-defining operating system for deal professionals — the platform where the world's most sophisticated private equity sponsors, lenders, and advisors manage every stage of their financing workflows.
Founded in 2019 by Dipish Rai (Harvard MBA, IIT, Columbia; ex-Providence Equity) and Vishal Singh (Columbia MS CS; ex-CTO Link3D, acquired by Nasdaq: MTLS), Termgrid has achieved what few fintech companies dream of: 4 of the top 5 global private equity firms trust us as their core technology partner. 1600+ institutions. 30,000+ professionals.
The Role
We are adding intelligent recommendations to the platform: quietly surfacing the most relevant connections, counterparties, and opportunities to users at the right moment, drawn from social signals (who is connected to whom, network activity) and transaction signals (who transacts with whom). The bar is subtle and genuinely useful discovery, not a salesy push.
You will be the first data scientist building this, from the ground up, on data most companies would love to have. It is a build-and-own role for someone who ships.
- Build and own the first recommendation features, surfacing relevant connections, counterparties, and opportunities inside the product.
- Apply classical recommendation techniques well: collaborative filtering, clustering, nearest neighbors, and standard ML models, on social and transaction data.
- Start simple, then layer in embeddings, similarity search, and ranking where they earn their place.
- Handle sparse data and cold-start for new users and entities.
- Keep recommendations subtle, relevant, and trustworthy: helpful discovery in a finance product, not a sales pitch. This is a product and UX judgment call as much as a modeling one.
- Own evaluation: offline metrics plus online experiments tied to real engagement and adoption, not vanity numbers.
- Partner with backend engineering on serving, and set light, useful ML practices as the first data scientist in this area.
- Solid, hands-on experience building recommendation or personalization systems in production.
- Strong grasp of classical recommendation methods: collaborative filtering, clustering, nearest neighbors, and standard ML models.
- Recommendation foundations: embeddings and similarity or vector search, ranking, feature engineering, and rigorous evaluation.
- Strong Python (pandas, numpy, scikit-learn) and strong SQL.
- Experimentation literacy: you design and read A/B tests and know the difference between offline and online lift.
- Product and business judgment, plus pragmatism: you can make recommendations feel subtle and relevant rather than salesy, you start simple, ship, and improve.
- Nice to have: capital markets, private credit, or fintech domain exposure; graph or network methods (graph embeddings, GNNs, link prediction) as a bonus for future sophistication; early or founding data scientist experience.
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