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.
Similar Jobs
Cloud • Information Technology • Security • Software
Lead the design and delivery of machine learning and LLM solutions for analytics automation and business decision-making. Own the AI stack roadmap from prototyping through deployment and impact measurement, build real-time and asynchronous ML pipelines, establish standards for evaluation, observability, security, and cost management, and mentor analysts. Translate complex technical work into strategic recommendations while partnering across functions in a fast-paced B2B SaaS environment.
Top Skills:
Agent Orchestration FrameworksAi AgentsData GovernanceData OrchestrationData TransformationEmbeddingsFeature EngineeringLlm ApisMachine LearningMl PipelinesMlopsModel MonitoringPythonReal-Time InferenceRetrieval-Augmented Generation (Rag)Semantic LayersSQL
Artificial Intelligence • Fintech • Machine Learning • Software
Support client and internal data science engagements by building, validating, and explaining predictive models, scorecards, decision rules, segmentation analyses, simulations, and business insights. Use Python for data preparation and analysis, communicate technical findings to technical and non-technical stakeholders, contribute to workshops and demonstrations, and document reproducible solutions. Collaborate globally across sales, product, technology, and applied intelligence teams while applying model governance, monitoring, testing, and delivery best practices.
Top Skills:
APIsCi/CdClaude CodeCodexCopilotMlflowMlopsOpencodePython
Marketing Tech • Software
Build and validate production machine learning and statistical models for music and entertainment marketing. Develop real-time data systems, predictive scoring, trend detection, AI briefing classification, dashboards, and GenAI-assisted workflows. Collaborate with data engineering, paid media, and creative teams while implementing tokenization, anonymization, and cross-border data privacy controls. The role is a 12-month fixed-term contract supporting clients across APAC.
Top Skills:
Ai Coding AssistantsDatabricksLarge Language Models (Llms)Machine LearningPythonSQL
What you need to know about the Mumbai Tech Scene
From haggling for the best price at Chor Bazaar to the bustle of Crawford Market, the energy of Mumbai's traditional markets is a key part of the city's charm. And while these markets will always have their place, the city also boasts a thriving e-commerce scene, ranking among the largest in the region. Driven by online sales in everything from snacks to licensed sports merchandise to children's apparel, the local industry is worth billions, with companies actively recruiting to meet the demands of continued growth.
