Junior Data Scientist

Cobblestone Energy

Key Responsibilities:

• Data Preparation: Collect, clean, and structure large datasets from market, fundamental, and operational sources
• Forecasting & Modeling: Develop and validate predictive models (e.g. wind generation, demand) to support trading strategies
• Strategy Research: Prototype, back-test, and refine data-driven trading ideas; collaborate with analytics and development teams to productionise solutions
• Infrastructure: Build and maintain data pipelines, dashboards, and automated tooling to streamline workflows
• AI-Accelerated Workflows: Utilise LLMs and agentic frameworks to accelerate exploratory data analysis (EDA), automate data cleaning pipelines, and generate synthetic data for model robustification.
• Stay current with the rapidly evolving AI landscape (e.g., RAG, Agents, Multimodal models) and proactively propose new ways to apply these to trading data.
• Collaboration: Partner with traders, risk managers, and software engineers to integrate insights into real-time decision-making
• Documentation: Maintain clear records of methodologies, data definitions, and model specifications
• Culture: Champion our values through proactive communication and teamwork

Your background:

• Bachelor's or master s degree in Computer Science, Mathematics, Statistics, or a related field
• Strong proficiency in programming languages such as Python and R
• Experience with data analysis and manipulation tools such as Pandas, NumPy, and SQL
• Knowledge of data visualization tools such as Matplotlib and Seaborn
• Experience programmatically integrating LLMs (via APIs, LangChain, or Python scripts) into data pipelines rather than just using chat interfaces.
• Proven ability to build custom "Agents" or automated workflows that interpret unstructured data or reason through multi-step problems.
• Pro-active and flexible work mentality
• Interest in data-driven trading
• Fluency in English, both written and spoken.

Preferred Background:

• Proven track record of excellence, academic or professional, in quantitative or analytical roles
• Experience working with time-series data, machine learning libraries, or cloud-based data platforms
• Demonstrated ability to innovate and drive end-to-end solutions