Data Science & Machine Learning Engineer

Job Title: Data Science & Machine Learning Engineer Location: Juno Beach, FL, 33408 Duration: 12+ Months (possible extension of long term) Local Candidates Only. Job Description: Data Science & Analytics & Machine Learning for Power Commodity Trading Focus is on the Data Science 1. Core Qualifications Master's or PhD in Data Science, Computer Science, Finance, Engineering, or Applied Mathematics 5+ years of experience in machine learning or advanced analytics, with at least 2 years in energy, power, or commodities markets Proficiency in Python (pandas, NumPy, scikit-learn, PyTorch, TensorFlow) and SQL Experience deploying production-grade ML and analytics systems in AWS, Azure, or GCP Strong foundation in statistics, linear algebra, and optimization 1. Domain Expertise – Power & Commodities: Understanding of power market dynamics (generation, transmission, demand forecasting, ISO/RTO markets such as PJM, ERCOT, MISO, CAISO) Familiarity with trading instruments: DA/RT markets, FTRs, CRRs, PPAs, futures, and swaps Knowledge of natural gas, renewables, and carbon markets Experience modeling locational marginal prices (LMP), congestion, and portfolio risk Understanding of regulatory and compliance data (FERC, EIA, ISO market data) 1. Machine Learning & AI Expertise Experience with supervised, unsupervised, and reinforcement learning for market forecasting or optimization Proficient in LLMs (GPT, Llama, Mistral) and RAG (Retrieval-Augmented Generation) pipelines for automating report generation, trade rationale summaries, or market insights Hands-on experience fine-tuning or evaluating generative models for quantitative or text-based analytics Familiarity with agentic AI frameworks (LangChain, LlamaIndex, CrewAI) for autonomous data gathering, analysis, and decision support Strong understanding of feature engineering, model interpretability, and bias control 1. Agentic & Autonomous Decision Systems Experience creating intelligent trading assistants or agent frameworks that monitor, forecast, and act based on real-time data Familiarity with planning, memory, and multi-agent collaboration concepts Implementation of guardrails and ethical constraints in autonomous AI systems 1. Forecasting & Quantitative Modeling Time series modeling (ARIMA, Prophet, LSTM, XGBoost) for price, load, or renewable generation forecasting Optimization and scenario modeling for trading positions, hedges, or dispatch strategies Proficiency with stochastic modeling, Monte Carlo simulations, and VaR analysis Ability to integrate weather data, grid conditions, and market signals into predictive systems 1. Data Infrastructure & Engineering Strong data architecture skills: ETL/ELT pipelines, dbt, Airflow, or Prefect Experience with data warehouses (DataBricks, Snowflake) Familiarity with vector databases (FAISS, Pinecone, Weaviate) for retrieval-augmented analytics Data governance awareness: versioning, lineage, security, and compliance 1. Analytics, Visualization & Communication Strong experience with dashboards and visualization tools (Power BI, Tableau, Plotly) Ability to design KPIs and visual analytics for trading performance, market exposure, and forecast accuracy Experience building automated insight pipelines or LLM-based analytics assistants Skilled in translating technical findings into clear narratives for traders and executives