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.
Skills Required
- 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
- Foundations in embeddings, similarity or vector search, and ranking
- Experience handling sparse data and cold-start problems
- Strong Python skills (pandas, numpy, scikit-learn)
- Strong SQL skills
- Experimentation literacy: design and read A/B tests and measure online lift
- Product and business judgment; pragmatic, ships simple solutions and iterates
- Domain exposure to capital markets, private credit, or fintech
- Experience with graph or network methods (graph embeddings, GNNs, link prediction)
- Early or founding data scientist experience
What We Do
Termgrid is a category-defining operating system for deal professionals in private capital markets. The platform specializes in streamlining deal management and execution for private equity sponsors, lenders, and advisors, with a focus on private credit and direct lending. By centralizing workflows for origination, diligence, approval, closing, and portfolio management, Termgrid helps organizations improve efficiency and reduce execution risk.
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