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MI
Mondelez International
Modelling & Simulation Scientist, Computational Physics
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What they do
An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.
$129,894 / year median in New Jersey
Job Description
Job Description Are You Ready to Make It Happen at Mondelēz International? Join our Mission to Lead the Future of Snacking. Make It With Pride. Working as part of a cross-functional team covering broad modelling disciplines , you are an internally recognized expert in computational physics , a technical area of critical importance to the R D function. You identify , define and solve complex modelling and simulation problems, tackling R D's highest priority ingredient, product, process, and packaging challenges. Applying your in-depth knowledge of this technical specialty, you will drive rapid, high-quality innovation capabilities across Mondelez's portfolio, spanning chocolate, biscuit , gum, and candy categories. How you will contribute You will bring computational physics modelling & simulation capabilities to Mondelez R D's highest priority areas, developing solutions to move R D work away from large, factory-scale trials towards small-scale or in silico predictions. Specifically, you will: Design and deliver rapid simulation capabilities to drive solutions to business challenges. Plan, lead, and manage modelling and simulation projects, scoping and proposing opportunities for sustained business impact. Work closely with engineers and scientists to define complex technical challenges and agree on problem scope. Communicate complex problems simply to non-technical audiences with relevant business context . Define the data required to build models; collaborate with internal teams or external organizations to collect this information, which may include designing experimental tests. Use software tools to design, build, test, and implement simulations - or select and manage delivery through an external partner. E nsure simulations are validated against experimental data, including any required experiments or factory trials. Deploy models via simple UIs using the Mondelez-approved technology stack, in collaboration with IT and/or external partners. Establish strategy and roadmap for integrating computational physics into R D business processes. Quantify and communicate the value of computational physics to stakeholders, the associated plan, and embed learnings into business processes to accelerate innovation and optimization. Author best-practice documentation and knowledge- base articles from project learnings. Share knowledge and coach junior colleagues, building internal modelling & simulation capability across R D. Maintain data management practices - ensuring models, datasets, and simulation outputs are documented and stored in line with company data governance policies. Research and keep pace with developments in relevant modelling, simulation, and virtual prototyping techniques. Provide regular project updates to senior stakeholders, flagging potential impediments and resource needs. What you will bring Physics & Modelling Fundamentals Fundamental understanding of materials' physical properties: how these are measured and modelled ( e.g. viscosity, thermal conductivity, rheology). Solid understanding of how physical transformations can be modelled ( e.g. conduction of heat, fluid mixing, phase change). Ability to decompose complex engineering problems into tractable steps and manage technical ambiguity. Ability to create and validate models to test and predict real-world scenarios. Programming & Software Proficient in Python for scientific computing, data analysis, and model deployment ( e.g. NumPy, SciPy, pandas, matplotlib). Familiarity with additional languages such as MATLAB, C++, or Julia is advantageous . Experience with machine learning and surrogate modelling frameworks ( e.g. scikit-learn, TensorFlow, or PyTorch ). Version control proficiency using Git / GitHub / GitLab for collaborative code management. Simulation & Engineering Tools Experience in one or more of the following: Game-engine physics: Unreal Engine, PhysX, Unity Computational fluid dynamics: ANSYS Fluent, COMSOL Multiphysics Finite element analysis ( ANSYS, Abaqus ) Process modelling: gPROMS , Witness, Aspen Discrete element method: Rocky