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Remote role, common 'Data Scientist' title, and mid-level scope increase competition despite niche recommender focus.
Core recommender and ML skills transfer across industries, though fintech and capital-markets experience is preferred.
Requires specific recommender experience, strong Python and SQL, and experimentation literacy, so filtering is moderately strict.
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Build and own the first recommendation system features to surface relevant connections, counterparties, and opportunities within the platform.
Apply and extend classical and advanced recommendation techniques like collaborative filtering, embeddings, similarity search, and ranking using social and transaction data.
Own evaluation metrics including offline and online experiments to measure real user engagement and adoption.
Hands-on experience building recommendation or personalization systems in production.
Strong expertise in classical recommendation methods (collaborative filtering, clustering, nearest neighbors) and standard ML models.
Proficiency in Python (pandas, numpy, scikit-learn) and SQL.
Work Experience Required: Not explicitly mentioned in the JD.
Data scientist capable of shipping product features independently, balancing product/UX judgment with modeling rigor for subtle, relevant recommendations.
Experience with rigorous evaluation methods including A/B testing and distinguishing offline vs online metrics.
Background or interest in finance, capital markets, private credit, fintech preferred but not mandatory; familiarity with graph or network-based methods is a plus.
