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Job Description: You will work with Being part of the refining Team of bp. These teams provides daily operational data management, data engineering and analytics support to this organization across a broad range of disciplines, applications and business requirements. Let me tell you about the role: A data scientist applies scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Their key responsibilities include collecting and analyzing large sets of data, using machine learning algorithms, statistical models, and data processing techniques to predict future trends and provide actionable insights. A machine learning engineer designs and develops artificial intelligence (AI) systems that can learn and make decisions autonomously. Their key responsibilities include creating and optimizing machine learning models, developing algorithms that enable machines to perform tasks without explicit programming, and working with large datasets to train these models. They collaborate with data scientists, software engineers, and domain experts to implement machine learning solutions that address specific business needs. Additionally, machine learning engineers are responsible for ensuring the scalability and efficiency of machine learning systems, continuously improving model performance through rigorous testing and validation, and staying updated with the latest advancements in the field to integrate cutting-edge techniques into their work. What you will deliver Part of a cross-disciplinary team, working closely with other data scientists, data engineers software engineers, data managers and business partners. Build scalable, re-usable, impactful data science products, usually containing statistical or machine learning algorithms, in collaboration with data engineers and software engineers. Carry out data analyses to yield actionable business insights. Adhere to and advocate for data science best practices (e.g. technical design, technical design review, unit testing, monitoring & alerting, checking in code, code review, documentation). Present results to peers and senior management. Actively contribute to improve developer velocity. Mentor others. What you will need to be successful (experience and qualifications) MSc or PhD degree in a quantitative field. Hands-on experience designing, planning, prototyping, productionizing, maintaining and documenting reliable and scalable data science products in complex environments. Applied knowledge as part of a team (if not leading) of data science tools and approaches across all data lifecycle stages. Thorough understanding of underlying mathematical foundations of statistics and machine learning. Development experience in one or more object-oriented programming languages (e.g. Python, Go, Java, C++) Basic SQL knowledge. Customer-centric and pragmatic mindset. Focus on value delivery and swift execution, while maintaining attention to detail. Strong stakeholder management and ability to lead large organizations through influence. Continuous learning and improvement mindset. Desired Skills: Experience with big data technologies (e.g. Hadoop, Hive, and Spark) is a plus. Knowledge of experimental design and analysis is a plus. Familiarity and experience with common Energy domain data objects and formats About bp Our purpose is to deliver energy to the world, today and tomorrow. For over 100 years, bp has focused on discovering, developing, and producing oil and gas in the nations where we operate. We are one of the few companies globally that can provide governments and customers with an integrated energy offering. Delivering our strategy sustainably is fundamental to achieving our ambition to be a net zero company by 2050 or sooner!