Quantitative Trading Researcher

BEO (formerly Co-Power); München; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

BEO in Munich is seeking a quantitative researcher to build and operate the battery trading engine. You will translate price forecasts and market data into systematic signals, backtest strategies, and run them live in Day-Ahead and Intraday markets for a fleet of behind-the-meter batteries.

You bring 4+ years in energy or financial markets, strong time-series skills, production-grade Python, and a hands-on mindset in an early-stage environment.

Qualifikationen

• 4+ years in quantitative research or applied data science with trading algorithms in energy or financial markets.
• Strong time-series fundamentals with backtesting and walk-forward validation.
• Proficient Python and production-grade code practices.
• Understanding of European power market mechanics (Day-Ahead, Intraday) and trading concepts.
• Comfort in an early-stage, founder-led environment and pragmatic decision-making.

Aufgaben

• Own and advance the trading strategy: end-to-end from research to live trading.
• Optimize dispatch and bidding across Day-Ahead and Intraday markets.
• Own risk and market exposure, manage position sizing and balancing energy costs.
• Coordinate the portfolio across sites to maximize risk-adjusted return.

Kenntnisse

Quantitative research

Time-series modeling

Backtesting

Python

Production-grade code

Energy/Financial markets

Jobbeschreibung

About BEO

BEO is a new kind of power company: battery-backed and vertically integrated across hardware, software, and financing.

The world is in the middle of an energy transition. We're building out renewables but we're not there yet. Today, electricity is costly and volatile. Industrial prices in Europe have doubled since 2021. That's a €100+ billion disadvantage for European companies. Daily price swings now top 500%, so industry can't plan. The scarce resource is no longer generating power. It's storing it. Storage moves power across time.

So we put a battery next to each factory. We finance it all. We run it with our AI-powered optimization engine that buys power when it's cheap, stores it, shaves peak loads, and trades what's left. The customer gets no CapEx, no complexity, just a lower and predictable energy bill. And we don't stop at single sites. Our network runs them as one connected fleet. The more batteries we connect, the more flexibility we pool, and the more value each customer gets.

The 20th century was driven by oil. The 21st will be driven by electricity. We are looking for people who want to build the power company of the future with us.

What you will do

This is the next wave of battery trading. Not standalone front-of-the-meter assets like most optimizers trade, but behind-the-meter batteries co-located at industrial sites, optimized under load, uncertainty, and operational constraints.

• Own and advance the trading strategy: Set the technical direction for our algorithmic trading stack and turn price forecasts and live market data into systematic trading signals that generate alpha from battery flexibility. You own this end to end: from research and signal generation to backtesting and running it live in the market.
• Optimize dispatch and bidding across markets: Formulate and deploy stochastic optimization that converts those signals into optimal battery dispatch and bids across Day-Ahead and Intraday, trading 24/7 under uncertainty and real operational constraints.
• Own risk and market exposure: Manage position sizing, market exposure, and imbalance/balancing energy (Ausgleichsenergie) costs so revenue is risk-adjusted, not just maximized
• Coordinate the portfolio and scale: As the fleet grows, coordinate decisions across all sites to maximize risk-adjusted return and pool flexibility

What we're looking for

• 4+ years in quantitative research or applied data science, with meaningful time developing trading algorithms in energy or financial markets, with a strong bias toward having shipped and run things in production
• Strong fundamentals in time-series modeling, with backtesting practice (risk-adjusted metrics, overfitting controls, walk-forward validation)
• Python and software-engineering practice: clean, testable, production-grade code, code review, and taking ML and/or optimization models from research into production
• A working understanding of European power market mechanics (Day-Ahead, Intraday) and of trading mechanics: price formation, execution, slippage, risk, and position sizing
• Comfort in an early-stage environment where the stack is being built from scratch. You scope pragmatically, make opinionated decisions, and don't wait for perfect requirements

About us

We are a small team taking on a hard problem, and we hold a high bar. We value ownership, direct and honest communication, and getting better every day. If you like solving complex problems and want to build something big, you'll fit right in. Our office is at Goetheplatz in central Munich. We offer plenty of flexibility but ask for an office-first commitment: when you're not traveling, most of your time is in the office. People learn faster and get more done together.