What You'll Do
- Build and run the data pipelines that feed the fund: prices, volumes, fundamentals, and filings from market data providers (e.g., Polygon, LSEG) and SEC EDGAR.
- Build scrapers that collect earnings materials and investor relations documents from listed companies.
- Design and maintain the fund's data lake, so the investment team can query clean and reliable data.
- Build the tools behind the strategy: screenings, signal testing, backtests, and a portfolio simulator that accounts for real trading costs.
- Connect our systems to broker APIs for execution, reconciliation, and reporting.
- Use AI tools to sharpen analysis, productivity, and insights.
What You'll Need to Succeed
- Python and working knowledge of SQL.
- Experience building data pipelines, scrapers, or API integrations, from work, personal projects, or competitions.
- Comfort with data analysis: you know when a result is too good to be true.
- High agency: you take ownership and move things forward on your own.
- Adaptability and comfort in a fast-changing environment.
- Genuine interest in financial markets and in using AI in your daily workflow. You don't need market experience, you need curiosity to learn it fast.
- Upper-intermediate to advanced English.
Nice to Have
- Experience with cloud data stacks (e.g., AWS, GCP, BigQuery) and pipeline orchestration.
- Experience with financial or market data (tick data, fundamentals, filings).
- Backtesting or quant work: Kaggle competitions, trading bots, portfolio tools you built yourself.
- Experience at fintechs, funds, brokerages, or data-heavy companies.