Lead Data Scientist / ML Engineer - Remote Role

DISCOVERED; Remote; AED 293,000 - 441,000; Full time

Job summary

A financial technology company in the United Arab Emirates is seeking a skilled Machine Learning Engineer with over 5 years of experience in applied machine learning and quantitative finance. The role involves designing hybrid models that leverage supervised learning and NLP to derive insights from various financial data sources. Candidates should possess strong Python and SQL abilities, with familiarity in frameworks like PyTorch and TensorFlow. This position offers opportunities for collaboration with product and engineering teams to deliver advanced analytical solutions.

Qualifications

• 5+ years of experience in applied machine learning, quantitative finance, or credit-risk modeling.
• Strong skills in Python and SQL with ML frameworks like PyTorch, TensorFlow, Scikit-learn, or XGBoost.
• Hands-on experience with financial data related to fixed income, private credit, or structured products.

Responsibilities

• Design and build hybrid ML models using supervised learning and NLP.
• Integrate data from various sources with emphasis on data freshness.
• Work closely with product and engineering teams to deploy models through APIs.

Skills

Applied machine learning

Quantitative finance

Credit-risk modeling

Python

SQL

PyTorch

TensorFlow

Scikit-learn

XGBoost

Time-series forecasting

Regression/classification

Probabilistic modeling

Job description

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, 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.

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).

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.).