The AI Industry Researcher will be working with the Strategy and Innovation AI Research team to identify, evaluate and develop advanced Analytical, Machine Learning and AI Solutions for Schneider Electric's AI HUB and its internal and external customers. The core challenges will be to identify and develop strategic, innovative and disruptive solutions for Schneider Electric and its customer by leveraging the Schneider DNA and cutting-edge AI/ML technologies.

Key Responsibilities:

• Take ownership for an entire project workstream or work with your colleagues jointly on a bigger project.
• Provide AI Research Services to Schneider Electric Business, Clients and Partners
• Develop Next Generation AI and Data Products together with Schneider Electric Clients and Partners
• Research and survey industrial reports and technical papers on latest industrial trends, case studies, models and methodologies in the areas of team's research.
• Successfully develop, conceptualize and test various statistical, AI and machine learning solutions to solve the future challenges.
• Integrate the outcomes into the existing Schneider EcoStruxure Landscape to elevate Schneider Electric's ability to create value for clients in areas and through means not immediately apparent to clients
• Researches, develops and maintains machine learning and statistical models for business requirements.
• Partners with lines of business to translate business analytic problems into technical solutions and actionable recommendations across the organization
• Work across the spectrum of statistical modelling including supervised, unsupervised, & deep learning techniques to apply the right level of solution to the right problem
• Build frameworks leveraging APIs to industrialize AI models across the organization
• Coordinate with different functional teams to monitor outcomes and refine/ improve the machine learning models
• Build frameworks leveraging APIs to industrialize AI models across the organization
• Collaborate with data and software engineers to enable deployment of models that will scale across the company's ecosystem
• Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown)

• Master's or Ph.D. in Computer Science, Statistics, Engineering, Physics, Mathematics, Economics.
• One or more year of Data Science experience for Ph.D. candidates and more than 3 years for master's candidates
• Minimum 3 years of experience in at least one of the following - Supervised and Unsupervised Learning, Classification Models, Cluster Analysis, Neural Networks, Non-parametric Methods, Multivariate Statistics, Reliability Models, Markov Models, Stochastic models, Bayesian Models, Genetic Algorithms, Fuzzy Logic, Inference Systems
• Experience with deep learning architecture and models such as CNN/RNN/LSTM/Transformer, time series, graph neural networks.
• Minimum of 3 years of experience in various statistical and machine learning models, data mining, unstructured data analytics in corporate or academic research environments
• Minimum 3 years of experience in data science workflows and methodologies, and tools such as Python, MATLAB, C++, Java, SQL, Visual Studio Code, Jupyter Notebooks, TensorFlow, PyTorch.
• Basic knowledge of Microsoft Azure, AWS, or other cloud platforms.
• Strong verbal and written communication skills in English

• 3 or more years of work and/or research experience in relevant domains (Energy, Industrial Automation, Oil & Gas, Chemicals, Natural Resources, Mining & Metals, Utilities or Power) - with hands on experience handling data driven decisions.
• Experience in consulting or relevant client facing engagements.
• Ability to think creatively to solve real world business problems.
• Ability to work in a global collaborative team environment
• Experience leading or collaborating with a team of data scientists in developing and delivering machine learning models that work in a production setting
• Candidates with experiences in one or more following areas, acquired through a combination of courses, academic projects and/or industrial projects, are strongly preferred:
  • Scientific AI/ML such as data-driven modeling, digital twins, reduced-order models (ROMs), and physics-informed neural networks (PINNs)
  • Deep reinforcement learning such as value-based algorithms (DQN...), policy gradients algorithms (A3C, DDPG, PPO...), model-based RL and various model-free algorithms.
  • Deep generative models such as Autoregressive, VAEs, GANs, score-based models, diffusion models.
  • Control theory and its applications such as Model Predictive Control and Kalman Filters
  • Advanced simulation of industrial process and physical systems.
  • Optimization such as linear, nonlinear, and mixed-integer programming.