Data Scientist / Quantitative Risk Analyst

Madfish; Remote; GBP 50,000 - 80,000; Full time

Job summary

A leading SaaS company in the United Kingdom is seeking a skilled Data Scientist with proficiency in Python and SQL to engineer risk-focused features. The candidate should have 4-6+ years in data science or risk analytics, and hands-on experience with credit-risk models. The role includes developing dashboards and data pipelines and working within a low-ego, high-ownership culture. This position emphasizes clear communication and teamwork in a remote setting.

Qualifications

• 4 – 6+ years in data science, risk analytics, or credit-modeling.
• Strong Python and SQL; solid PySpark on distributed data a big plus.
• Hands-on experience building or validating credit-risk or fraud models.

Responsibilities

• Engineer risk-focused features in Python/PySpark.
• Develop PD/LGD models using various statistical techniques.
• Create robust, reproducible data pipelines with version control.

Skills

Python

SQL

Risk analytics

Data science

Git

Statistics

PySpark

Tools

PostgreSQL

PowerBI

Job description

About Forecasa

Forecasa is a profitable, founder‑led SaaS company that turns raw real‑estate transaction data into decision‑grade intelligence for hedge funds, private‑lenders, and MBS desks. We move fast, value autonomy with accountability, and maintain a culture where clear documentation beats hierarchy.

What you’ll do

• Engineer risk‑focused features (borrower, lender, property, geography) in Python/PySpark.
• Develop and validate PD / LGD models using WoE, IV, logistic GBM, XGBoost, or similar.
• Prototype lender‑health metrics (capital‑diversification, portfolio turnover, market concentration, etc.) for client dashboards.
• Create robust, reproducible data pipelines (git‑versioned, unit‑tested, CI in GitLab).

• Produce concise notebooks & dashboards that can feed automated PDF reports.

Must‑have qualifications

• 4 – 6+ years in data science, risk analytics, or credit‑modeling.
• Strong Python (pandas, NumPy, scikit‑learn) and SQL; solid PySpark on distributed data a big plus.
• Hands‑on experience building or validating credit‑risk or fraud models (PD, scorecards, Basel/IFRS 9, etc.).
• Fluency in statistics (inferential tests, multicollinearity, model monitoring).
• Git workflow, code review discipline, and comfort with Agile/Kanban boards.
• Clear written & spoken English; able 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, etc).
• Prior work in a fully remote, internationally‑distributed team.

How we work

• Stack: Python • PySpark • PostgreSQL/Snowflake • GitLab CI • AWS & on‑prem Spark
• Communication: Slack, Zoom, Notion. Meetings kept lean; deliverables drive the schedule.
• Culture: Low‑ego, high‑ownership. We favor clarity, rapid feedback loops, and well‑documented processes.