Senior Data Scientist

Remote Worker LTD.; Sunbury-on-Thames; Hybrid; GBP 90,000 - 140,000; Full time

Benefits offered by this job

Competitive compensation

Global scale ML problems

Culture of scientific rigour and learn

Hybrid working arrangements

Career development pathways

Job summary

bp is seeking a Senior Data Scientist who combines deep machine learning expertise with strong software engineering to design, build, and deploy production-grade ML systems.

This role goes beyond traditional ML engineering, applying ML as a core field to develop novel algorithms and models that are deployed as scalable products across NLP, optimisation, simulation, or generative AI. You will work in a cross-disciplinary team of data scientists, software engineers and data engineers.

Qualifications

• MSc, PhD degree or equivalent experience in a quantitative field (e.g. Computer Science, Mathematics, Physics, Engineering, or related discipline).
• Hands-on experience designing, prototyping, productionising, maintaining, and scaling ML/data science products in complex environments.
• Strong and demonstrable expertise in machine learning algorithms, statistical modelling, and optimisation techniques - with a track record of applying these to build production-grade solutions.
• Applied knowledge of data science and ML tools across all stages of the data and model lifecycle.
• Thorough understanding of the mathematical foundations of statistics, machine learning, and scientific computing.
• Strong programming experience in one or more object-oriented languages (e.g. Python, Go, Java, C++).
• Advanced SQL knowledge.
• Experience with modern ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring.
• Knowledge of experimental design, analysis, and scientific methodology.
• Customer-centric and pragmatic mentality with a focus on value delivery and swift execution, while maintaining rigour and attention to detail.
• Strong partner management and ability to influence across teams and organisations.
• Continuous learning and improvement mentality.

Responsibilities

• Design, build, and maintain scalable, production-grade machine learning systems and pipelines using modern engineering practices (CI/CD, testing, monitoring, observability).
• Apply machine learning science to develop novel algorithms and models that are deployed as reliable, scalable products - not limited to experimentation but extending through to production delivery and operational use.
• Build impactful ML products demonstrating statistical modelling, deep learning, and AI techniques across operational, scientific, and R&D domains.
• Translate complex scientific and business problems into well-scoped ML solutions, delivering actionable insights and deployable capabilities.
• Architect and optimise ML systems for performance, scalability, and reliability in production environments.
• Collaborate closely with data scientists, data engineers, software engineers, and domain authorities as part of cross-disciplinary teams.
• Adhere to and advocate for engineering and data science guidelines (technical design, design reviews, unit testing, monitoring & alerting, code reviews, documentation).
• Present technical results, trade-offs, and product outcomes to peers and senior partners.
• Actively supply to improving developer velocity, engineering standards, and shared tooling.
• Mentor junior team members and chip in to the technical growth of the wider team.

Skills

Python

Go

Java

C++

SQL

MLOps

CI/CD

Unit testing

Monitoring

Education

MSc/PhD in CS or related

Tools

Cloud Platforms

CI/CD tools

NoSQL databases

Spark

Hadoop

Job description

Entity

Technology

Job Family Group

IT&S Group

Job Description

Equal Opportunity Employer

bp is an equal opportunity employer. We believe that diversity and inclusion drive innovation and are essential to our success. We welcome applications from all qualified individuals regardless of race, colour, religion, gender, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected characteristic.

We are committed to making reasonable adjustments for candidates with disabilities or long-term conditions. If you require any adjustments during the recruitment process, please let us know.

Role Summary

We are looking for a Senior Data Scientist who combines deep machine learning expertise with strong software engineering subject area to design, build, and deploy production‑grade ML and AI systems.

This role goes beyond traditional ML engineering. You will apply machine learning science as a core field - developing novel algorithms and models that are not only experimentally validated but architected and deployed as scalable, reliable products. Whether it's advancing NLP, optimisation, simulation, or generative AI, you will deliver solutions that transition seamlessly from research to production and create measurable value.

You will work as part of a cross‑disciplinary team alongside data scientists, software engineers, data engineers, and domain authorities - translating complex scientific and business problems into deployable ML products.

Key Responsibilities

• Design, build, and maintain scalable, production‑grade machine learning systems and pipelines using modern engineering practices (CI/CD, testing, monitoring, observability).
• Apply machine learning science to develop novel algorithms and models that are deployed as reliable, scalable products - not limited to experimentation but extending through to production delivery and operational use.
• Build impactful ML products demonstrating statistical modelling, deep learning, and AI techniques across operational, scientific, and R&D domains.
• Translate complex scientific and business problems into well‑scoped ML solutions, delivering actionable insights and deployable capabilities.
• Architect and optimise ML systems for performance, scalability, and reliability in production environments.
• Collaborate closely with data scientists, data engineers, software engineers, and domain authorities as part of cross‑disciplinary teams.
• Adhere to and advocate for engineering and data science guidelines (technical design, design reviews, unit testing, monitoring & alerting, code reviews, documentation).
• Present technical results, trade‑offs, and product outcomes to peers and senior partners.
• Actively supply to improving developer velocity, engineering standards, and shared tooling.
• Mentor junior team members and chip in to the technical growth of the wider team.

Qualifications

Essential

• MSc, PhD degree or equivalent experience in a quantitative field (e.g. Computer Science, Mathematics, Physics, Engineering, or related discipline).
• Hands‑on experience designing, prototyping, productionising, maintaining, and scaling ML/data science products in complex environments.
• Strong and demonstrable expertise in machine learning algorithms, statistical modelling, and optimisation techniques - with a track record of applying these to build production‑grade solutions.
• Applied knowledge of data science and ML tools across all stages of the data and model lifecycle.
• Thorough understanding of the mathematical foundations of statistics, machine learning, and scientific computing.
• Strong programming experience in one or more object‑oriented languages (e.g. Python, Go, Java, C++).
• Advanced SQL knowledge.
• Experience with modern ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring.
• Knowledge of experimental design, analysis, and scientific methodology.
• Customer‑centric and pragmatic mentality with a focus on value delivery and swift execution, while maintaining rigour and attention to detail.
• Strong partner management and ability to influence across teams and organisations.
• Continuous learning and improvement mentality.

Desired

• Experience with big data technologies (e.g. Hadoop, Hive, Spark).
• Experience with generative AI, LLMs, or retrieval‑augmented generation (RAG).
• Exposure to Agentic AI concepts, including autonomous agents, tool use, and orchestration frameworks.
• Experience applying machine learning and AI to scientific or R&D workflows - with emphasis on building deployable ML products from scientific research (e.g. simulation, optimisation, physics‑informed models).
• Familiarity with model interpretability, uncertainty quantification, and advanced experimental methodologies.
• Consistent track record of publications, invention disclosures (IDFs), or patents in machine learning or AI.
• No prior experience in the energy industry required.

What We Offer

• Competitive compensation and benefits package.
• Opportunity to work on innovative ML and AI problems at global scale.
• A culture that values scientific rigour, engineering excellence, and continuous learning.
• Hybrid working arrangements and a commitment to work‑life balance.
• Career development pathways in a world‑class technology organisation.

Please note that the business title for this role, which will be reflected in the employment contract, is Senior Machine Learning Engineer.

Please note that roles based out of SJS or Sunbury will move to Timber Square, Southwark, from Q4 2027.

Why join us?

At bp, we support our people to grow in a diverse and exciting environment. We believe that our team is strengthened by diversity.

There are many aspects of our employees' lives that are meaningful, so we offer benefits to enable your work to fit with your life. These benefits can include flexible working options, a generous paid parental leave policy, excellent retirement benefits, among others!

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Travel Requirement

Negligible travel should be expected with this role.

Relocation Assistance

This role is not eligible for relocation.

Remote Type

This position is a hybrid of office/remote working.

Skills

Cloud Platforms, Cloud Platforms, Collaboration, Communication, Configuration management and release, Continuous deployment and release, Creating a high performing team, Database Design, Digital Project Management, Documentation and knowledge sharing, Emerging technology monitoring, Facilitation, Information Security, Mentoring, Metrics definition and instrumentation, NoSql data modelling, Problem Solving, Relational Data Modeling, Risk Management, Scripting, Secure development, Service operations