{"id":3671327,"url":"https://alion.io/job/novartis-scientific-computing-engineer-drug-product-process-modeling-data-science","title":"Scientific Computing Engineer - Drug Product Process Modeling & Data Science","company":{"id":5824,"name":"Novartis","domain":"novartis.com","url":"https://alion.io/company/novartis","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":84,"open_postings":76,"ghost_share":0,"stale_share":0.658,"repost_share":0,"time_to_fill_p50_days":26,"computed_at":"2026-11-05T05:54:30Z"}},"role":"Science & Research","role_family":"Science & Research","seniority":"junior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Design of Experiments","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Digital Twin","optional":true},{"name":"Time Series","optional":true}],"status":"closed","first_seen_at":"2026-11-03T09:11:04Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-04T18:50:37Z","board_verified":false,"closed_at":"2026-11-04T18:50:37Z","days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Job Description Summary\nWe are looking for a hands-on Scientific Computing Engineer to strengthen process modeling, statistics, and data science capabilities in Drug Product development. This role is intended for an early-career engineer with a strong quantitative foundation, practical programming skills, and the ability to translate formulation and process questions into data- and model-based solutions.The ideal candidate has a background in mechanical engineering, process engineering, chemical engineering, or a closely related engineering discipline, and is motivated to work at the interface of pharmaceutical formulation, powder technology, process understanding, statistics, and modern data science.\nExperience with AI or machine learning is welcome, but not the primary selection criterion. We expect that a candidate with strong engineering judgment, solid mathematics, and a fast-learning mindset can acquire the required AI methods on the job.\nWhat you will do\nYou will work hands-on with formulation scientists, process engineers, data scientists, and manufacturing experts to develop practical modeling and analytics solutions for drug product development. The focus is on building useful tools, models, analyses, and workflows that improve process understanding and support decisions from laboratory studies through scale-up.\nJob Description\nMajor Accountabilities\nDevelop and apply mechanistic, empirical, statistical, and hybrid modeling approaches to support drug product formulation and process development, especially for process understanding, scale-up, and manufacturing-relevant questions.\n\nTranslate formulation and process questions into model- and data-ready problem statements; define success criteria, assumptions, and uncertainty considerations with subject-matter experts.\n\nApply statistics, Design of Experiments, multivariate analysis, and data-driven modeling to plan experiments, analyze results, and accelerate learning cycles.\n\nBuild predictive models and decision-support tools for key drug product unit operations, with particular interest in oral solid dosage forms, powder technology, formulation, and process engineering.\n\nBuild end-to-end data science solutions including data preparation, exploratory analysis, modeling, validation, deployment, and lifecycle management, with a focus on transparency and reproducibility.\n\nCreate clear visualizations, dashboards, and technical narratives to communicate insights and support decision making for diverse stakeholders.\n\nContribute to automation and AI-assisted workflows for data preparation, modeling, analysis, and reporting, while maintaining scientific oversight and practical usability.\n\nContribute to knowledge sharing, documentation, internal standards, and reusable modeling/AI assets within the global modeling and digital community.\n\nEssential Skills\nMaster’s degree or PhD in mechanical engineering, process engineering, chemical engineering, pharmaceutical engineering, materials science, applied mathematics, statistics, data science, or a closely related quantitative engineering discipline.\n\nEarly-career profile preferred, typically with 2-4 years of relevant industry experience after a master’s degree or 0-4 years after a PhD, and a clear motivation for hands-on modeling, coding, and applied problem solving.\n\nCore skills\n\nStrong engineering and mathematical foundation, including process science, transport phenomena, statistics, numerical methods, and/or mechanistic modeling.\n\nMust have hands-on programming experience in Python or a similar programming language, with the ability and motivation to become productive in Python very quickly if not already fluent.\n\nExperience applying statistics, DoE, data analysis, simulation, optimization, and/or machine learning to engineering or scientific problems.\n\nAbility to work with experimental and industrial datasets, including data cleaning, exploratory analysis, and uncertainty-aware interpretation including model credibility assessments according to regulatory guidelines & standards.\n\nStrong communication skills to explain technical concepts to non-experts and influence decisions.\n\nDigital & AI capabilities (beneficial; can be developed on the job)\n\nBasic experience with machine learning, model evaluation, or AI-enabled analytics is an advantage, but less important than strong engineering fundamentals, coding ability, and learning agility.\n\nInterest in AI-assisted modeling, automation, and agent-based workflows, with willingness to learn and apply these methods in a scientifically rigorous way.\n\nUnderstanding of model lifecycle management, reproducibility, and deployment considerations in regulated environments.\n\nExperience with visualization and storytelling, such as dashboards or clear technical reporting.\n\nDesirable Skills\nExperience or academic exposure to powder technology, formulation science, oral solid dosage forms, pharmaceutical unit operations, process modeling tools, PBM, DEM, gPROMS, or digital twins.\n\nExposure to QbD principles, PAT concepts, or regulatory-relevant modeling activities.\n\nExperience working in global matrix organizations.\n\nSkills Desired\nBiostatistics, Curious Mindset, Data Governance, Data Governance Framework, Data Literacy, Data Science, Data Visualization, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Statistical Analysis, Time Series Analysis","description_format":"text","description_chars":5443,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Oncology Therapeutics","Neurology & CNS Therapeutics","Immunology & Inflammation Therapeutics","Cardiometabolic 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