Data Scientist / Quantitative Risk Analyst

Forecasa is a profitable, founder-led SaaS company transforming real-estate transaction data into decision-grade intelligence for hedge funds, private lenders, and MBS desks. As a Data Scientist / Quantitative Risk Analyst, you will engineer risk-focused features and develop credit risk models using advanced statistical and machine learning techniques.

• Engineer risk-focused features (borrower, lender, property, geography) in Python/PySpark
• Develop and validate PD / LGD models using WoE, IV, logistic regression, GBM, XGBoost, or similar techniques
• Prototype lender-health metrics (capital diversification, portfolio turnover, market concentration) for client dashboards
• Create robust, reproducible data pipelines that are git-versioned, unit-tested, with CI in GitLab
• Produce concise notebooks and dashboards that feed automated PDF reports

• 4-6+ years in data science, risk analytics, or credit modeling
• Strong Python (pandas, NumPy, scikit-learn) and SQL proficiency
• Solid PySpark experience on distributed data
• Hands-on experience building or validating credit-risk or fraud models (PD, scorecards, Basel/IFRS 9)
• Fluency in statistics (inferential tests, multicollinearity, model monitoring)
• Git workflow, code review discipline, and comfort with Agile/Kanban boards
• Clear written and spoken English with ability to summarize findings for non-technical stakeholders
• Nice-to-haves: Familiarity with U.S. mortgage or private-lending data, experience with Postgres, MinIO/S3, or dbt, knowledge of BI/visualization tools (Plotly, PowerBI, Looker)

Work Environment: Fully remote, internationally-distributed team. Stack includes Python, PySpark, PostgreSQL/Snowflake, GitLab CI, AWS, and on-prem Spark. Communication via Slack, Zoom, and Notion with lean meetings driven by deliverables.

Culture: Low-ego, high-ownership environment favoring clarity, rapid feedback loops, and well-documented processes.