Lead Engineer, Data Analytics

The Impact You'll Make As our Lead Engineer, Data Analytics , you will make sure that everything we model, predict, and optimize is grounded in real thermodynamics and real-world system behaviour. Our digital twins only create value when they accurately represent the physical systems behind them — from chillers and pump skids in data center cooling loops, to transcritical CO₂ refrigeration systems, heat pumps, and heat recovery circuits. Your impact? You'll own the physical foundation of our digital solutions: defining how systems should be represented, challenging results that don't respect physical principles, and turning deep engineering knowledge into optimization opportunities that can be quantified, validated, and defended with customers. This is a unique opportunity to bridge Danfoss' application engineering expertise with data analytics and digital solutions — helping us understand how systems perform today, identify how they could perform better, and turn that insight into measurable energy and cost savings. What You’ll Be Doing Own the thermodynamic foundation of our digital twins, defining how refrigeration, cooling, heat pump, and heat recovery systems are represented, which assumptions apply, and where a model reaches its limits. Identify and quantify system optimization opportunities across customer applications, translating engineering analysis into measurable impact in terms of energy, efficiency, CO₂ emissions, and cost. Set the technical direction for domain modelling and validation, including simulation approaches, accuracy targets, validation methods, and acceptance criteria, while mentoring the wider team in applying sound physical reasoning. Work with stakeholders across product lines and business areas to understand requirements and identify scalable optimization opportunities that can be applied across customer systems. Safeguard measurement and data integrity, challenging telemetry that does not match physical expectations, defining virtual sensors where instrumentation is missing or unreliable, and determining which data is trustworthy enough to use for modelling and optimization. What We're Looking For We're looking for an experienced engineer who combines deep thermodynamic knowledge with analytical thinking, practical problem-solving, and the ability to influence others. Deep knowledge of thermodynamics and a degree in Mechanical, Energy, Process, Chemical Engineering, or a related engineering discipline. Working proficiency in Python, SQL, Bash, or another programming language for engineering data analysis, with the ability to work comfortably with complex and imperfect time-series plant data using tools such as pandas, NumPy, and standard plotting libraries. A proven track record of delivering quantified improvements, ideally through projects that have led to measurable improvements in processes, products, energy efficiency, or system performance. The…