Data Scientist

Termgrid

IndiaremotePosted 1 month ago

Skill Required

Data-ScienceMachine-LearningRecommendation-SystemsProduct-Data-ScienceApplied-Data-ScienceData-ScientistSearch-Data-ScientistAI-Data-ScientistData-Science-ExpertData-Science-SpecialistML-Data-ScientistStatistical-Data-ScientistResearch-Data-ScientistData-Scientist-AnalyticsData ScientistScikit-learnEngineeringbuildingfintechPythonPandasdesignNumPyDesign PatternsRustSQLandMachine LearningFulltime

Key highlights

First data scientist role
Build-and-own responsibility
Focus on recommendation/personalization systems
Required proficiency in Python and SQL

Role overview

Termgrid is rewriting the rules of private capital markets with a category-defining operating system for deal professionals, trusted by 4 of the top 5 global private equity firms, 1600+ institutions, and 30,000+ professionals. Founded in 2019 by Dipish Rai and Vishal Singh, the company is now adding intelligent recommendations to the platform to surface relevant connections, counterparties, and opportunities based on social and transaction signals. You will be the first data scientist building this from the ground up, acting in a build-and-own role for someone who ships, focusing on subtle and genuinely useful discovery rather than a salesy push.

Responsibilities

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.

Requirements

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