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Strong employer brand and Bangalore location increase candidate density, but PhD research specialization limits pool.
High because role requires deep chemometrics, analytical chemistry, and domain-specific industrial process knowledge.
High due to mandatory PhD, proven publications, and specialized chemometrics and ML expertise.
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Develop and implement advanced chemometric, statistical, and machine learning models (including physics- and chemistry-informed AI) for chemical and process systems in industrial energy contexts.
Collaborate with chemists, engineers, and domain experts to translate complex chemical and process data into actionable insights supporting process understanding, optimization, monitoring, and R&D innovation.
Ensure model credibility via validation, uncertainty quantification, and interpretability, deploying scalable solutions suitable for noisy and biased datasets and real-time environments.
PhD in Applied Statistics, Data Science, Chemometrics, Mathematics, Engineering, or related field with relevant experience in large-scale parameterization and process data modeling/optimization.
Proven expertise in chemometric/multivariate statistical methods applied to analytical chemical data (chromatography, spectroscopy) and industrial process modeling.
Experience with deterministic and stochastic optimization techniques as well as AI methods including reinforcement learning, GANs, meta-learning, and generative AI applied to industrial processes.
Work Experience Required: Significant experience demonstrated by track record of impactful R&D and consultancy projects; notice period: Not explicitly mentioned in the JD.
Strong interdisciplinary technical operator comfortable working at the interface of mathematics, statistics, chemistry, engineering, and digital technologies in multidisciplinary teams.
Experienced in translating physicochemical and process variability into structured analytical problems and scalable computational solutions across the industrial energy sector.
Demonstrated strategic impact through development of robust, interpretable AI-enabled analytics with deployment focus and a record of scholarly publication in relevant scientific domains.