About the role
At Kpler, we are dedicated to helping our clients navigate complex markets with ease. By simplifying global trade information and providing valuable insights, we empower organisations to make informed decisions in commodities, energy, and maritime sectors. Since our founding in 2014, we have focused on delivering top-tier intelligence through user-friendly platforms. Our team of over 700 experts from 35+ countries works tirelessly to transform intricate data into actionable strategies, ensuring our clients stay ahead in a dynamic market landscape. Join us to leverage cutting-edge innovation for impactful results and experience unparalleled support on your journey to success. \n Your future position 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, utilizing our internal Linear Programming (LP) model as a core tool for tuning and optimization. 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, utilizing the internal Linear Programming model for tuning. Prioritize 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 optimize refinery circuit views and regional supply/demand dynamics. Source and leverage large datasets using Python, PostgreSQL and 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.