Data Science Researcher

<p>About Us</p><p>We are building the future of retail investing with our Vibe Trading AI Agent. Our mission is to democratize sophisticated quantitative trading by translating natural language goals into fully automated, backtested, and risk-managed investment strategies. Powered by a novel Model Context Protocol (MCP), our platform allows anyone, regardless of their coding ability, to deploy institutional-grade trading logic. We are a team of first-principles thinkers and seasoned engineers from top tech and finance firms, aiming to redefine the human-computer interface for financial markets.</p><p>Key Job Duties</p><p> Natural Language Strategy Translation: Develop and train state-of-the-art NLP and Large Language Models (LLMs) to interpret user commands, goals, and risk constraints (e.g., "trade ETH breakouts on high volume but keep my max drawdown under 15%").</p><p> Quantitative Strategy Modeling: Construct systematic trading strategies from the ground up, covering everything from feature engineering and signal generation to execution logic.</p><p> Portfolio & Risk Modeling: Build sophisticated risk management frameworks, including dynamic position sizing algorithms, portfolio optimization, and the calculation of metrics like Value at Risk (VaR) and Conditional Value at Risk (CVaR).</p><p> Backtesting Engine Validation: Collaborate with engineers to ensure the statistical robustness of our backtesting systems, accounting for real-world frictions like slippage, fees, and latency.</p><p>Requirements</p><p> Bachelor or course in data science, statistics or mathematics</p><p> 1+ year of experience in Crypto investment</p><p> Entry-level proficiency in Python and its libraries (e.g., Pandas, NumPy, Scikit-learn etc).</p><p> Entry-level Foundation in time-series analysis, statistics, and probability theory</p><p> Familiarity with financial datasets (e.g., L1/L2 order book data, on-chain analytics)</p><p> Proven ability to conduct independent research, formulate hypotheses, and drive projects from initial ideation to production-level modeldeployment.</p>