Senior Data Scientist (AI/ML)

TL;DR

Senior Data Scientist (AI/ML): Research, architect, and deploy machine learning models for credit scoring, cash flow forecasting, and autonomous capital allocation workflows with an accent on production-ready AI systems and intelligent multi-agent orchestration. Focus on building interpretable models, managing full MLOps lifecycle, and translating complex outputs into actionable insights for credit and investment decisions.

Company

World's leading AI-powered private credit firm supporting growth companies globally with breakthrough decision science technology.

What you will do

• Build and deploy models for credit scoring, cash flow forecasting, risk classification, and portfolio optimization to inform underwriting and lending.
• Design data-driven agents with financial guardrails and human-in-the-loop controls for treasury and capital allocation.
• Orchestrate multi-step workflows using LLM pipelines to analyze market data, hirify.global, and portfolio signals.
• Create dashboards and reports translating model outputs for credit and executive stakeholders.
• Manage model lifecycle with experiment tracking, versioning, deployment, and monitoring for reliability.
• Develop model-serving APIs in event-driven cloud architectures, collaborating with engineering.

Requirements

• 6+ years in Data Science, Quantitative Modeling, or AI/ML, including 2–4 years deploying production ML models, agentic AI, or LLM pipelines.
• Proficiency in time-series forecasting, credit scoring, regression, classification, optimization; tree-based models (XGBoost, LightGBM) and SHAP interpretability.
• Advanced Python (Pandas, Scikit-learn); MLOps (MLflow); SQL; Postgres/MySQL/Databricks.
• Dashboarding (Streamlit, Tableau, Power BI); strong software engineering practices.
• AI fluency to translate business problems into AI directives; communication to present to stakeholders.

Nice to have

• Master's/Ph.D. in quantitative field (CS, Stats, Math, Finance).
• FinTech, private credit, or quantitative finance experience.
• Docker, CI/CD, AWS Lambda/Serverless, observability (Langfuse, CloudWatch, Datadog).
• NoSQL (MongoDB, Neo4j, vector DBs); REST APIs, ETL design; FastMCP tooling.