Computational Data Science Researcher

, India

Job Family Group:

Research and Development

Worker Type:

Regular

Posting Start Date:

July 6, 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.

What you’ll be doing

• Design, develop, and implement advanced chemometric and hybrid modeling frameworks for chemical and process systems, including statistical latent-variable-based methods, Bayesian statistical learning, physics- and chemically-informed machine learning approaches, spectral and chromatographic modeling, and uncertainty quantification.