AI Industry Consulting Analyst, Rueil-Malmaison

Company Overview

Schneider Electric’s purpose is to empower all to make the most of our energy and resources, bridging progress and sustainability for all. At Schneider, we call this Life Is On. We are driving digital transformation by integrating the most advanced energy and automation technologies. We connect products, control platforms, software and services throughout the lifecycle of our customers' activities to the cloud for integrated management of residential housing, commercial buildings, data centers, infrastructures and industries. Artificial intelligence has the potential to transform industries and help unlock efficiency and sustainability. You' re interested in solving real world problems with AI? Join our AI Hub to create a sustainable, digitized, new electric future. We combine our long‑standing manufacturing and domain expertise with cutting‑edge innovation in AI, machine learning, and deep learningto empower smarter decision‑making, agility, and decarbonization.Role Overview

The AI Industry Analyst will work with the New Value Streams team to identify, evaluate and develop advanced analytical, machine learning and AI solutions for Schneider Electric’s AI Hub and its clients. The core challenges will be to find creative solutions for client challenges by leveraging the Schneider DNA and the wealth of methodologies from the AI/ML world.Responsibilities

Work collaboratively on larger AI industry projects. Provide AI consulting services to Schneider Electric clients and partners. Develop AI and data strategies together with clients and partners. Conceptualise and test various statistical, AI and machine learning solutions to solve the challenge at hand.Integrate outcomes into the existing EcoStruxure landscape to elevate Schneider Electric’s ability to create value in ways not immediately apparent to clients. Research, develop and maintain machine learning and statistical models for business requirements. Partner with lines of business to translate business analytic problems into technical solutions and actionable recommendationsacross the organization.Apply appropriate statistical modelling, including supervised, unsupervised and deep learning techniques. Build frameworks leveraging APIs to industrialise AI models across the organisation. Coordinate with functional teams to monitor outcomes and refine/improve models. Collaborate with data and software engineers to enable deployment of models that scale across the company ecosystem.Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown). Basic Qualifications

Master’s or Ph.D. in Computer Science, Statistics, Engineering, Physics, Mathematics or Economics. Minimum 1 year of data science experience. Minimum 1 year of work experience in relevant domains (Energy, Oil&Gas, Chemicals, Natural Resources, Mining&Metals, Utilities or Power) with hands‑on experience handling data‑driven decisions. Minimum 1 year of experience in supervised and unsupervised learning, classification, clustering, neural networks, non‑parametric methods, multivariate statistics, reliability models, Markov models, stochastic models, Bayesian models, deep learning, genetic algorithms, fuzzy logic, inference systems.Minimum 1 year of experience in data mining, unstructured data analytics in corporate or academic research environments. Minimum 1 year of experience in consulting or client‑facing engagements. Minimum 1 year of experience with statistical software (Python, R, Octave, TensorFlow, Caffe or Theano).Minimum 1 year of programming experience in Python, R or similar. Preferred Qualifications

Experience in AI and data consulting. Familiarity with relational databases and intermediate level SQL knowledge. Creative problem‑solving for real‑world business challenges. Ability to work in a global collaborative team environment. Experience leading or collaborating with a data science team to deliver production‑ready models.Knowledge of UNIX or Linux environments. Experience working with large datasets and tools like MapReduce, Hadoop, Hive or Spark, Flink. Proficient verbal and written communication skills in English.

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