Data Scientist

Number of Vacancies: 1

Inside IR35 – 6 Months Contract (With likely extension)

Day Rate: As per Market Rate

Location: Central London

Hybrid: 2 Days On‑Site per Week

Note: Must have a valid Right to Work in the UK — no visa sponsorship available.

Overview

We are seeking a highly capable Data Scientist to join the Supply, Trading & Shipping (ST&S) organisation and help build next‑generation AI‑powered analytics, decision‑support tools, and data‑driven insights. This role sits at the intersection of advanced analytics, modern AI techniques, and complex trading domain challenges.

You will work closely with engineers, product managers, data engineers, and trading stakeholders to design, develop, and deploy scalable data science solutions that deliver measurable business value. The ideal candidate combines strong analytical and modelling expertise with practical engineering skills and a deep understanding of how to operationalise AI in production environments.

Key Responsibilities

AI & Data Science Delivery

·       Develop, validate, and deploy machine learning models, including LLM‑powered components, predictive models, optimisation algorithms, and decision‑support systems.

·       Build robust data pipelines for feature engineering, model training, evaluation, and monitoring.

·       Apply modern AI techniques such as retrieval‑augmented generation (RAG), embeddings, vector search, and agent‑based systems where appropriate.

·       Conduct exploratory data analysis to uncover insights, trends, and opportunities across trading, risk, and operational datasets.

·       Translate complex business problems into analytical frameworks and measurable outcomes.

Engineering & Productionisation

·       Collaborate with engineering teams to integrate models into production systems with strong reliability, observability, and performance.

·       Implement best practices for MLOps, including CI/CD for models, automated evaluation, and monitoring of model drift and data quality.

·       Work with cloud‑native environments (Azure/AWS) to scale data science workloads efficiently.

·       Ensure solutions are secure, compliant, and aligned with enterprise architecture standards.

Stakeholder Engagement

·       Partner with trading, risk, operations, and product teams to understand use cases, refine requirements, and validate outputs.

·       Communicate complex analytical concepts clearly to both technical and non‑technical audiences.

·       Contribute to shaping the roadmap for AI and analytics capabilities within ST&S.

Innovation & Best Practices

·       Stay current with emerging AI/ML techniques, but prioritise practical, high‑impact application over research experimentation.

·       Contribute to internal knowledge bases, reusable components, and best‑practice frameworks to scale data science across the organisation.

·       Identify opportunities to leverage enterprise AI platforms, unstructured data services, and streaming capabilities.

Skills & Experience

Required

·       Graduation in Information Technology/ Computer Science

·       Minimum 8 years of experience

·       Strong proficiency in Python for data science, including libraries such as Pandas, NumPy, Scikit‑learn, PyTorch, or TensorFlow.

·       Experience building and deploying ML models into production systems.

·       Familiarity with LLMs, embeddings, vector databases, RAG patterns, and AI agents.

·       Strong understanding of data modelling, statistics, and machine learning fundamentals.

·       Experience working with cloud platforms (Azure or AWS) and modern DevOps/MLOps practices.

·       Ability to balance analytical rigour with delivery speed in fast‑moving environments.

Desirable

·       Experience with AI/ML frameworks such as LangChain, LlamaIndex, Haystack, Hugging Face, Weaviate, or similar.

·       Knowledge of unstructured data processing, streaming architectures, or real‑time analytics.

·       Multi‑year experience as a Data Scientist, ideally within trading, commodities, energy, or other complex data environments.

·       Domain knowledge in commodity trading, ETRM systems, or market analytics.

·       Understanding of data governance, responsible AI, and regulatory considerations.