Computational Chemometrics Researcher

Responsibilities

• Collaborate closely with chemists, process engineers, and domain experts to translate physicochemical behavior, operating regimes, and sources of variability into structured analytical problems and computational solutions.
• Develop robust, scalable, and deployable models capable of handling noisy, sparse, and biased datasets, ensuring stability and reliability across varying operating conditions and enabling application in real-time or near-real-time environments.
• Ensure model credibility through rigorous validation against experimental, simulated, and operational data, maintaining consistency with chemical and physical scientific principles, incorporating constraints, quantifying uncertainty, and preserving interpretability.
• Apply chemometric and statistical methods to enhance process understanding by identifying drivers of variability, detecting anomalies, performing root-cause analysis, and supporting process monitoring, control, optimization, and scale-up activities.
• Drive innovation in AI-enabled data analysis by exploring and applying advanced techniques such as deep learning for spectroscopy, manifold learning, probabilistic modeling, and physics-informed or chemistry-constraint-guided machine learning tailored to chemical systems.
• Contribute to scientific leadership and organizational knowledge by publishing research, developing intellectual property, and supporting internal knowledge-sharing initiatives to strengthen capabilities in digital chemistry and advanced analytics.
• What you bring
• PhD in Applied Statistics, Data Science, Chemometrics, Mathematics, and/or Engineering, with experience in large-scale parameterization and process data modeling and optimization.
• Preference for candidates with strong AI expertise and hands-on experience in advanced areas such as reinforcement learning, GANs, meta-learning, active learning, and generative AI, applied to industrial process modeling and optimization.
• Proven experience in deterministic and stochastic optimization.
• Experience in analytical chemistry techniques in chromatography and spectroscopy, with strong expertise in applying chemometric and multivariate statistical methods to analytical chemical data for modelling, prediction and energy system monitoring purposes.
• Knowledge of design of experiments, general linear modeling, multivariate analysis, statistical modeling, and/or time series analysis is advantageous.
• Demonstrated strong problem-solving skills, with the ability to work both independently and collaboratively within a team environment.
• Excell

Additional Information

, India Job Family Group: Research and Development Worker Type: Regular Posting Start Date: June 3, 2026 Business Unit: Projects and Technology Experience Level: Experienced Professionals Job Description: What's the role The Chemometrics & Digital Chemistry team develops and deploys advanced statistical and computational approaches to convert complex chemical and process data into actionable insights. Leveraging multivariate and Bayesian methods, chemometrics, and physics- and chemistry-informed AI, all integrated with first-principles understanding, the team enables operational excellence and drives innovation across Integrated Gas (LNG and GTL), Downstream, and Renewables & Energy Solutions (LCG, LCF, and CO₂ abatement). The Computational Chemometrician is an individual technical contributor and subject-matter expert who applies advanced analytics and modeling techniques to extract value from high-dimensional datasets, including chemical, physical, and process data. Operating at the interface of mathematics, statistics, engineering, chemistry and digital technologies, the role supports improved understanding, optimization, and control of complex industrial and energy systems. In this role, you will develop and implement chemometric, statistical, and machine learning models, including physics- and chemistry-informed AI solutions, embedded within scalable digital workflows. These solutions enable process understanding, monitoring, and optimization, as well as quality control, predictive maintenance, and R&D innovation. You will work with diverse data sources, such as sensor data, engineering signals, spectroscopy, and chromatography, and translate analytical outputs into clear, actionable insights for scientists, engineers, and business stakeholders.

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