Senior Quantitative Analyst

About RobotBulls RobotBulls is a multinational fintech startup building automated AI trading systems that leverage market volatility to generate alpha for our users. We develop trading algorithms in Python that combine big data analytics, alternative data, and machine learning to deliver best-in-class market predictions. Our mission is to democratize the efficiency of automated trading, making sophisticated quantitative strategies accessible without requiring users to write code. We are now expanding our research team and seeking a Senior Quantitative Analyst to drive the next generation of our predictive models and trading strategies. The Role As a Senior Quantitative Analyst at RobotBulls, you will be responsible for designing, testing, and deploying quantitative trading strategies across crypto and traditional equity markets. You will work closely with the CTO and engineering team to enhance our existing predictive models, incorporate alternative data sources (including sentiment and emotion-based signals), and expand our automated trading robot portfolio. This is a high-impact role in a small, agile team where your work will directly shape the product. Key Responsibilities Design and implement systematic trading strategies in Python, with a focus on exploiting market volatility and short-term price movements across crypto and equity markets. Develop and refine predictive models that incorporate historical market data, current market trends, and alternative data (including sentiment analysis scraped from financial websites and social media). Backtest, validate, and optimize strategies using rigorous statistical methods; monitor live performance and iterate based on empirical results. Build and maintain data pipelines for ingesting, cleaning, and processing large-scale financial and alternative datasets. Collaborate with engineering to productionize quant models into automated trading robots deployed on the RobotBulls platform. Research and integrate new data sources, features, and machine learning techniques (including NLP for sentiment extraction) to improve signal quality. Conduct ongoing research into market microstructure, regime detection, and risk management frameworks tailored to automated trading. Evaluate and incorporate decentralized finance (DeFi) protocols and on-chain data into trading strategies as RobotBulls expands into decentralized trading solutions. Produce research documentation, performance reports, and model explanations for internal stakeholders. Stay current with academic literature, industry developments, and emerging techniques in quantitative finance and AI-driven trading. Required Qualifications MSc or PhD in Quantitative Finance, Mathematics, Statistics, Physics, Computer Science, or a related quantitative discipline. 5+ years of professional experience in quantitative research, algorithmic trading, or systematic strategy development at a hedge fund, prop trading firm, or fintech. Expert-level Python programming, including NumPy, pandas, scikit-learn, and relevant ML frameworks (PyTorch, TensorFlow, or similar). Deep understanding of statistical methods, time-series analysis, econometrics, and machine learning applied to financial markets. Proven track record of developing profitable systematic strategies with verifiable performance metrics. Strong knowledge of crypto markets, exchange APIs, and on-chain data analytics. Experience with backtesting frameworks (e.g., Backtrader, Zipline, vectorbt, or custom in-house systems). Solid grasp of risk management, portfolio construction, and position sizing methodologies. Familiarity with alternative data sources, sentiment analysis, and NLP techniques for signal generation. Ability to work autonomously in a small-team, startup environment with minimal supervision. Preferred Qualifications Experience with high-frequency or mid-frequency trading strategies. Familiarity with DeFi protocols, decentralized exchanges, and on-chain analytics tools (e.g., Dune, Glassnode, Nansen). Knowledge of market microstructure and order execution optimization. Experience with cloud computing infrastructure (AWS, GCP) and containerization (Docker, Kubernetes). Prior experience at an early-stage fintech or trading startup. Publications or research in relevant areas (quantitative finance, ML for trading, market prediction). #J-18808-Ljbffr