Software Engineer in Data Science

Company Description Vitol is a leader in energy and commodities. Vitol produces, manages and delivers energy and commodities to consumers and industry worldwide. In addition to its primary business of trading, Vitol is invested in infrastructure globally, with $10+billion invested in long-term assets. Vitol’s customers include national oil companies, multinationals, leading industrial companies and utilities. Founded in Rotterdam in 1966, today Vitol serves its customers from some 40 offices worldwide. Revenues in 2023 were $400bn. For more information: vitol.com Job Description As our portfolio of work continues to grow, we are looking for an experienced Software Engineer to join our global data science and machine learning team, to help us: Lead: Make the right technology choices in the context of the team’s projects Design: Ensure we design and build the models and tools to meet the functional/non-functional requirements, as well as being supportable Explore: Help the team understand and exploit emerging technologies Improve: Improve our software engineering skillsets, and grow as a team The individual will work predominantly with our data scientists and machine learning engineers, but will also need to directly engage with the commercial teams (across trading, operations, support functions, etc.) as necessary. The role will also act as a bridge between the Data Science team and other technology teams for areas like application integration, data sourcing, infrastructure and tooling. For the successful candidate, this role will give them exposure across the machine learning lifecycle, being able to apply their skills wherever they can add value, from working with business stakeholders to help define the project, to data collation through to solution design and model implementation. We are looking for a candidate who brings both a breadth and depth of experience, from a theoretical and practical perspective; but equally someone who is able to and wants to continue learning. As a small team, everyone is expected to organise, prioritise and execute their own tasks; with a strong focus on maximising the business value from their actions. This means the individual will need to be comfortable working on multiple projects simultaneously, managing competing priorities and stakeholder requirements. The successful candidate will join a team of experienced, collaborative practitioners, who are (pragmatically) solving some of the most challenging and impactful problems the energy industry is facing; as well as pushing the boundaries around the ‘art of the possible’. Core Responsibilities include: In conjunction with the Data Scientists deliver models and solutions to business users, and other technology teams across a wide range of projects and technologies Develop, test, maintain software tools and data pipelines for machine learning Provide software engineering and design expertise and best practices (Python) with a focus on maintainability, performance, and reliability Take ownership of key technical infrastructure Engage with projects at any point in their lifecycle, understand and debug bespoke applications; driving performance and reliability Manage relationships and priorities across projects, focused on maximising value Actively participating in and leading code reviews, experiment design and tooling decisions to help drive the team’s velocity and quality Qualifications Essential Qualifications 3-5+ years in industry; fluency in Python with ability to design and write clean, modular, well-documented code and a solid understanding of coding best practices Master's degree in Computer Science or a related field Ability and desire to learn and apply new technologies Ability to logically evolve an architecture from prototype to product, considering technical debt and delivery risk Collaborative approach to problem solving - ability to effectively pair program Effective technical communicator - both written and verbal; able to translate loose designs into documentation / process / operating model Experience with data engineering, APIs, and cloud platforms (AWS/Azure/GCP) and containerization technologies (Docker) Experience with enterprise software development lifecycle and tooling including continuous integration and delivery concepts/technologies  Desired Experience Experience with machine learning workflows, cloud scale machine learning infrastructure (including LLMs), big data technologies (e.g. Spark) Experience in the energy or commodities trading industry, with knowledge of financial markets and trading concepts  Data orchestrators (Airflow, Dagster) and cloud-based ETL/ELT pipelines Personal Characteristics A self-motivated individual who thrives on seeing the results of their work make an impact in the business Strong communication skills, both verbally and in writing Proven ability to be flexible, work hard, and a sense for the art of the possible Methodical, organized and with an attention to detail - in general, in experimental design, and in code Willingness to share their knowledge and learn from others An interest in learning about the commodities space Resourceful, able to think creatively and adapt in a dynamic environment Team player, with an open non-political style and a high level of integrity Desire to be a thought-partner in a fast-growing team, and make an impact at a business that sits at the heart of the world’s energy flows Additional Information What we offer Competitive salary and benefits package Large diversity of projects with real-world impacts on a truly global scale Entrepreneurial environment within a flat hierarchy, where great ideas come to life quickly Close collaboration with various business units across our key regions (eg. London, Singapore, Houston, Geneva) A highly motivated DS and ML team comprised of experienced individuals with a supportive attitude and great team spirit Being part of the energy transition through increased emphasis on renewable & alternative energy sources at a pivotal moment in the industry Strong management commitment to incorporating machine learning into the future of Vitol’s operations