Cobblestone Energy
Finance
JuniorDataScientist
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optimal for Entry candidates.
“Junior Data Scientist at Cobblestone Energy. Skills: Data Science, Machine Learning, AI, Data Engineering. Collaborate with analytics and development teams. Productionise solutions”
Industry & Context.
Interpret unstructured data; Reason through multi-step problems
What They're Looking For.
Must Have
Bachelor's or master's degree, Proficiency in Python and R, Experience with Pandas, NumPy, and SQL, Knowledge of Matplotlib and Seaborn, Experience integrating LLMs into data pipelines, Ability to build custom Agents, Fluency in English
Nice to Have
Academic or professional excellence in quantitative roles, Experience with time-series data, Experience with machine learning libraries, Experience with cloud-based data platforms, Demonstrated ability to innovate, Demonstrated ability to drive end-to-end solutions
What You'll Do.
Collaborate with analytics and development teams
Productionise solutions
Maintain data pipelines
Build automated tooling
Maintain automated tooling
Utilise LLMs to accelerate EDA
Utilise LLMs to automate data cleaning
Utilise LLMs to generate synthetic data
Stay current with AI landscape
Propose new ways to apply AI
Partner with risk managers
Partner with software engineers
Integrate insights into decision-making
Maintain records of methodologies
Maintain records of data definitions
Maintain records of model specifications
Communicate proactively
How You'll Work.
Team & Collaboration
Analytics teams; Development teams; Traders; Risk managers; Software engineers
Communication Scope
Proactive communication; Written communication; Spoken communication
Full Job Description
Employment Type: Full-time 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 The Hiring Process Application Review Online Assessments: Psychometric and logical reasoning tests Case Study: Research-ori
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