Lead Data Scientist/ML Engineer - Remote
Remote / Online - Candidates ideally in
UAE/Dubai
Listed on 2026-01-12
UAE/Dubai
Listing for:
DISCOVERED
Remote/Work from Home
position Listed on 2026-01-12
Job specializations:
-
IT/Tech
Data Scientist, AI Engineer, Machine Learning/ ML Engineer, Data Analyst
Job Description & How to Apply Below
Job description
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, Pitch Book LCD, Securitize, Centrifuge, Maple, and True Fi, 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.
- 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, Tensor Flow, 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).
- 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, Lang Chain, Hugging Face, etc.).
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