Forecasa

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.

We're looking for a sharp, curious, and driven Data Engineer to join Forecasa, a U.S.-based data startup delivering high-quality real estate data and analytics to lenders and investors.

You'll be part of our Data Acquisition & Quality team, helping scale and improve the systems that collect, validate, and monitor the data powering our platform. We've built serious AI-augmented development workflows internally - Claude Code, autonomous agents, GitLab-based orchestration - and we're looking for engineers who've already formed their own opinions about how to work effectively with these tools.

What You'll Do

• Develop and maintain Python-based web scrapers, using AI coding assistants to accelerate development while applying your judgment on reliability and edge cases
• Use tools like Selenium, BeautifulSoup, Pandas, and PySpark to extract and normalize data efficiently
• Package scrapers as Docker containers and deploy them to Kubernetes
• Create and manage Airflow DAGs to orchestrate scraping pipelines
• Build data validation pipelines to catch anomalies, missing values, and inconsistencies
• Review and refine AI-generated code for production reliability
• Set up Grafana dashboards to monitor pipeline health and data quality metrics

Our Tech Stack

Python · PySpark · Selenium · Airflow · Pandas · Postgres · S3 · Docker · Kubernetes · GitLab · Grafana · Claude Code

What We're Looking For

• Solid Python experience, especially building web scrapers
• Familiarity with Selenium, BeautifulSoup, or Scrapy
• Some experience with Docker, Airflow, or other orchestration tools
• Active use of AI coding tools (Claude Code, Cursor, Copilot, etc.) with opinions about what works and what doesn't - we want to hear about your preferred workflows
• Strong code review instincts - you can spot issues in code whether you wrote it or an AI did
• A resourceful, problem-solving mindset - not afraid to dig into a messy site or debug a flaky scraper

Bonus Points For

• Experience with Grafana or Prometheus for monitoring
• Exposure to cloud platforms (AWS preferred) and managing scrapers at scale
• Familiarity with CI/CD and Git workflows (we use GitLab)

About Us

Forecasa delivers enriched real estate transaction data to private lenders and institutional investors. We're a small, fast-moving team with a strong engineering culture. We've invested heavily in AI-augmented development - autonomous coding agents, GitLab orchestration - and we're looking for people who are already bought in on this direction, not people we need to convince.

Location

Remote – we welcome candidates from anywhere in the world.

To Apply

Tell us about your current AI coding workflow - what tools you use, what you've learned, what you'd do differently. Generic applications without this will be deprioritized.