At a Glance
- Tasks: Own data integrity and quality control for refinery models using advanced analytics.
- Company: Join Kpler, a leader in real-time oil and chemicals analytics.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Dynamic team environment focused on collaboration and continuous improvement.
- Why this job: Be a key player in shaping innovative refinery models and analytics.
- Qualifications: 3-5 years in refining analysis with strong data skills and technical expertise.
The predicted salary is between 60000 - 80000 £ per year.
Kpler’s Oil & Chemicals business unit is rapidly advancing its real-time analytics and is looking for a technically sharp, data-literate Refineries Analyst to join its growing global team. In this role, you will take absolute ownership of the data integrity, maintenance, and quality control of our bottom-up refinery models. You will synthesize Kpler’s industry-leading proprietary data—including live crude flows, production, pipeline volumes, and inventory levels—into a cohesive modeling framework, utilising our internal Linear Programming (LP) model as a core tool for tuning and optimisation.
By connecting upstream inputs and offline events with real-world refinery capacities and yields, you will ensure our models seamlessly integrate into Kpler’s broader Supply & Demand balances. Because you are joining us during an ambitious product expansion, you will play a foundational role in the evolution of our platform. This is a truly hybrid-minded position where you will serve as the subject matter expert bridging the gap between physical market reality and technical architecture—partnering closely with our Product Engineering team to build, refine, and validate our most sophisticated fundamental datasets.
Your mission is to:
- Maintain data integrity, capacity, and offline event records for individual refinery models and regional balances, utilising the internal Linear Programming model for tuning.
- Prioritise the validation and improvement of existing models; data quality and domain expertise are the critical drivers for this role.
- Lead the effort to backtest and guide models using external data sources (EIA, JODI, IRR, ANP) to ensure outputs align with physical market realities.
- Apply a deep understanding of distillation, reforming, FCC, and blending to optimise refinery circuit views and regional supply/demand dynamics.
- Source and leverage large datasets using Python, PostgreSQL to automate data flows and improve model accuracy.
- Collaborate with Data, Product, Engineering, and Sales teams to provide insights for refineries product development.
- Respond to internal and external data requests and client queries in a timely manner.
Experience & Background
Essential:
- At least 3–5 years of experience as a Refinery Economist, Refining Analyst, LP Modeler or related roles.
- A deep understanding of physical refinery unit operations (Distillation, Reforming, FCC, Blending, etc.) and refinery economics (flows, pricing, regulations, and arbitrage dynamics).
- Practical experience using industry-standard Linear Programming software (Aspen PIMS, AVEVA Unified/Spiral, or Haverly GRTMPS) is essential to be functional in this role.
- Comfortable with Python and PostgreSQL.
- Proven experience sourcing and leveraging external data (EIA/JODI/IRR) to model refineries.
- Strong background in data-driven modeling, validation, and managing input data integrity (e.g., capacity and offline events).
Desirable:
- Experience with refinery optimization, especially with a focus on circuit-wide views.
- Experience with data visualization.
- Past experience contributing to SaaS product or data improvement initiatives.
- Experience in leveraging AI tools (Claude, Cursor, etc.) for data analysis & management, and general workflow optimization.
Behavioural Competencies:
- Ownership of day-to-day work and strong attention to detail.
- Self-starter, thrives in an ambitious, early‑stage product environment without requiring direct supervision.
- Eagerness to invest significant time in individual refinery tuning and bottom‑up modeling.
- Driven to find data to backtest and guide models.
- Client‑facing confidence.
Qualifications
Bachelor’s degree in a technical or quantitative field, with proven experience applying modeling techniques in commercial or research environments.
Kpler is committed to providing a fair, inclusive and diverse work environment. We believe that different perspectives lead to better ideas, and better ideas allow us to better understand the needs and interests of our diverse, global community. We welcome people of different backgrounds, experiences, abilities and perspectives and are an equal opportunity employer.
Refineries Data & LP Modeling Specialist employer: Medium
Kpler is an exceptional employer that fosters a dynamic and inclusive work culture, where innovation and collaboration are at the forefront. As a Refineries Data & LP Modeling Specialist, you will have the opportunity to take ownership of critical data processes while working alongside a global team of experts in a rapidly expanding sector. With a commitment to employee growth and development, Kpler offers unique advantages such as access to cutting-edge technology and the chance to contribute to meaningful projects that shape the future of the oil and chemicals industry.
StudySmarter Expert Advice🤫
We think this is how you could land Refineries Data & LP Modeling Specialist
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend relevant events, and connect with Kpler employees on LinkedIn. Building relationships can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio or case studies showcasing your experience with refinery models and data analysis. This will give you an edge during interviews and demonstrate your expertise.
✨Tip Number 3
Practice makes perfect! Get comfortable with common interview questions related to data integrity and LP modeling. Mock interviews with friends or mentors can help you articulate your thoughts clearly.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you're genuinely interested in joining our team at Kpler.
We think you need these skills to ace Refineries Data & LP Modeling Specialist
Some tips for your application 🫡
Show Your Data Skills:Make sure to highlight your experience with data integrity and modelling in your application. We want to see how you've used tools like Python and PostgreSQL to tackle real-world problems, so don’t hold back!
Tailor Your Application:Take a moment to customise your CV and cover letter for this role. Mention specific experiences that relate to refinery operations and LP modelling. We love seeing candidates who take the time to connect their background to what we do!
Be Clear and Concise:When writing your application, keep it straightforward and to the point. We appreciate clarity, so make sure your key achievements and skills stand out without unnecessary fluff.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands and shows us you’re serious about joining our team!
How to prepare for a job interview at Medium
✨Know Your Data Inside Out
Make sure you have a solid grasp of the data you'll be working with. Familiarise yourself with Kpler’s proprietary data sources and how they relate to refinery operations. Being able to discuss specific datasets and their implications during the interview will show your technical prowess.
✨Demonstrate Your Modelling Skills
Prepare to talk about your experience with Linear Programming models and any relevant software you've used, like Aspen PIMS or AVEVA. Bring examples of how you've applied these tools in past roles, especially in tuning and optimising refinery models.
✨Showcase Your Analytical Mindset
Be ready to discuss how you approach data validation and quality control. Highlight any experiences where you've backtested models using external data sources like EIA or JODI, and explain how you ensure that outputs align with physical market realities.
✨Emphasise Collaboration Skills
Since this role involves working closely with various teams, prepare examples of how you've successfully collaborated in the past. Discuss how you’ve partnered with engineering or product teams to enhance data integrity and model accuracy, showcasing your ability to bridge technical and market knowledge.