Research Executive

2 weeks ago

Bengaluru Karnataka, India Unilever Full time

**Background**: Unilever’s Personal Care Science & Technology (S&T) team develops future fit technologies that enable superior, benefit led products. The Physical Sciences platform combines deep expertise in chemistry, emulsions, materials, surface and colloid science, and process engineering with advanced data science, AI/ML, and modelling techniques to translate cutting edge science into scalable product solutions. Our models and tools support ingredient selection, formulation optimisation, and claim substantiation, and are delivered as governed, self service assets for scientists and partners across S&T. Research outputs typically include next generation product technologies scaled to at least pilot plant level, alongside novel claims, new methods, and reusable digital assets such as datasets, R&D tools, and dashboards. The team works closely with Design, Analytical, and Process groups to address complex product challenges, provide deeper insight into emerging technologies, and translate scientific understanding into consumer relevant performance at speed and scale. **Purpose**: **Main Accountabilities**: - Capture, organise, and standardise structured and unstructured data for modelling and insight generation. - Build and maintain predictive models (e.g., QSAR, ML, statistical) to support material and formulation innovation. - Curate and analyse experimental and literature data, ensuring data quality and integrity. - Develop user friendly tools, dashboards, and visualisations to make models and insights accessible to scientists. - Stay updated on emerging computational and AI/ML techniques relevant to chemistry and materials science; share best practices within the team. Skills & Experience Essential: - Degree in Chemistry, Chemical Engineering, Materials Science with experience in Data Science. - Hands-on experience in Python (and/or R) for data analysis and modelling, including data curation, robust train/validation/test splits, and model evaluation. - Exposure to RDKit and other descriptor generation techniques for QSAR or predictive modelling. - Comfortable working in Linux-based environments and using version control tools (e.g., Git). - Strong statistical and machine learning knowledge applied to chemical or formulation data. - Ability to communicate technical insights clearly to non-specialists. Desirable: - Experience deploying models as dashboards or lightweight tools (e.g., Dash, Flask, Power BI). - Understanding of model explainability and visualization techniques. - Awareness of molecular modelling concepts. **Our commitment to Equality, Diversity & Inclusion** Unilever embraces diversity and encourages applicants from all walks of life This means giving full and fair consideration to all applicants and continuing development of all employees regardless of age, disability, gender reassignment, race, religion or belief, sex, sexual orientation, marriage and civil partnership, and pregnancy and maternity. - "All official offers from Unilever are issued only via our Applicant Tracking System (ATS). Offers from individuals or unofficial sources may be fraudulent—please verify before proceeding."_

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