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What you’ll be working on:

• Designing and building hybrid ML models that combine supervised learning, time-series forecasting, and NLP to extract insights from unstructured data like PDFs, fund memos, and regulatory filings.

• Adding explainability to models using techniques like SHAP, LIME, and feature attribution so outputs are transparent and human-readable.

• Building scalable data pipelines across off-chain fundamentals, on-chain activity, and macro benchmarks.

• Integrating data from sources like FRED, PitchBook LCD, Securitize, Centrifuge, Maple, and TrueFi, with strong data lineage and freshness guarantees.

• Developing anomaly detection and reconciliation tools across issuer, administrator, and blockchain datasets.

• Creating evaluation frameworks to measure accuracy, confidence intervals, latency, and data quality.

• Backtesting model outputs against historical NAVs, secondary-market trades, and redemptions.

• Researching and incorporating credit-risk signals (CDS spreads, recovery rates, default data, etc.).

• Building continuous learning loops using live market data and partner feedback.

• Working closely with Product and Engineering to ship models via APIs, SDKs, and dashboards used by traders, curators, and risk teams.

• Collaborating with data providers, protocol teams, and fund administrators to improve coverage and signal quality.

• Partnering with the CTO on long-term model governance, transparency, and AI ethics.

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What I’m looking for:

• 5+ years of experience in applied ML, quantitative finance, or credit-risk modeling.

• Strong Python and SQL skills, plus experience with ML frameworks like PyTorch, TensorFlow, scikit-learn, or XGBoost.

• Solid understanding of time-series forecasting, regression/classification, and probabilistic modeling.

• Hands-on experience with financial data (fixed income, private credit, or structured products).

• Familiarity with blockchain and DeFi data, including smart contracts, token metadata, and on-chain events.

• Experience deploying ML models into production (APIs, orchestration, or streaming systems).

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Bonus to have:

• Background in credit analytics, NAV valuation, or structured credit

• Experience in quant research, fintech data science, or tokenized asset analytics

• Experience with NLP, vector databases, and LLMs / GenAI tools (OpenAI APIs, GPT-4, LangChain, HuggingFace, etc.)