Senior Data Scientist

Overview

As a Senior Machine Learning Engineer at bp, you design, build, and deploy production-grade ML and AI systems that turn research into scalable products. You collaborate with cross-functional teams to translate complex problems into deployable ML solutions across NLP, optimisation, simulation, and generative AI domains. You drive models from experimental validation to production delivery, delivering measurable value at global scale. You join a culture that values scientific rigour, engineering excellence, and continuous learning.

Pay / Benefits

• Competitive compensation and benefits package
• Work on innovative ML/AI problems at global scale
• Culture of scientific rigour and engineering excellence
• Hybrid working arrangements
• Career development pathways

Responsibilities

• Design and maintain scalable ML systems and pipelines with modern engineering practices (CI/CD, testing, monitoring)
• Develop novel ML algorithms and models deployed as reliable products from research to production
• Build ML products applying statistical modelling, deep learning, and AI techniques across domains
• Translate business problems into well-scoped ML solutions with actionable insights
• Architect and optimise ML systems for performance, scalability, and reliability in production
• Collaborate with data scientists, data engineers, software engineers, and domain experts
• Adhere to engineering and data science guidelines (design reviews, testing, monitoring, documentation)
• Present results and trade-offs to peers and senior leaders
• Improve developer velocity, standards, and shared tooling
• Mentor junior team members and contribute to technical growth

Key requirements

• MSc, PhD degree or equivalent experience in a quantitative field
• Hands-on experience designing, prototyping, productionizing, and scaling ML/data science products
• Strong expertise in ML algorithms, statistical modelling, and optimisation for production-grade solutions
• Applied knowledge of data science and ML tools across the data and model lifecycle
• Strong mathematical foundations of statistics, ML, and scientific computing
• Programming experience in Python, Go, Java, or C++
• Advanced SQL knowledge
• Experience with MLOps, model lifecycle management, CI/CD, and monitoring
• Knowledge of experimental design and scientific methodology
• Customer-centric and pragmatic mentality with value delivery and rigor
• Strong partner management and cross-team influence
• Continuous learning and improvement mindset

• customer-centric mindset
• pragmatic mentality
• strong collaboration and influencing across teams
• ML algorithms, statistical modelling, and optimisation techniques
• MLOps, model lifecycle management, CI/CD, monitoring
• Python, Go, Java, C++

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