What will you do?

Collaborate with the AI Product Owner to understand the business requirements and define appropriate modelling approaches, experimentation plans, and success metrics.

Coordinate with business teams to monitor model outcomes, gather feedback, and refine/improve machine learning models based on performance insights.

Lead data discovery, feature engineering, experimentation, offline/online evaluation, and productionization with CI/CD for ML; own model documentation, reproducibility, and traceability.

Apply supervised/unsupervised/deep learning, NLP, and LLM techniques (including RAG pipelines, prompt engineering, vector search, and safety guardrails) where they create clear value.

Design and execute rigorous evaluation strategies for ML and GenAI models, including offline metrics, human‑in‑the‑loop reviews for GenAI outputs, regression checks, and failure mode analysis.

Implement governance frameworks for AI models – applying bias/fairness checks, safety filters, responsible AI controls, and executing evaluation protocols defined by Business.

Collaborate with data/ML engineers to industrialize models via APIs/batch jobs, feature stores, scalable serving, and monitoring for drift, performance, cost, and latency.

Lead data mining, collection, and quality initiatives across structured, semi‑structured, and unstructured data to ensure integrity, lineage, and compliance.

Maintain rigorous experiment tracking using tools, ensuring reproducibility and clear lineage across model iterations and experiments.

Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown) Mentor and lead data scientists, conduct design/code reviews, and cultivate best practices in experimentation, evaluation, and documentation.

Track emerging tools/techniques in ML/GenAI and drive reusable frameworks, templates, and SDK/API‑based accelerators to industrialize solutions across the organization.

What skills and capabilities will make you successful?

Technical Experience:

5–8 years of hands-on experience across classical ML (tree‑based methods, GLMs), deep learning (Py Torch/Tensor Flow), and NLP/LLMs (tokenization, embeddings, fine‑tuning, instruction‑tuning, RAG).

Hands‑on with evaluation and safety/guardrail patterns for production GenAI.

Familiarity with ML lifecycle platforms (such as Sage Maker, Azure ML, or Databricks) to run experiments, track models, and provide well‑structured model artifacts to ML Engineers for deployment Comfortable with AWS services for data/ML (e.g., S3, Glue, EMR/Spark, Lambda, SageMaker; Databricks), and integrating with enterprise data lakes/warehouses.

Proficient in Python and ML/DS libraries (Pandas, scikit‑learn, Py Torch/Tensor Flow, XGBoost/LightGBM); strong software practices (testing, linting, packaging).

Strong SQL and data wrangling; experience with Spark/Databricks for large‑scale feature pipelines and training.

Working knowledge of data privacy, safe model behaviors, prompt filtering/output moderation, and auditability for regulated environments.

Exploratory data analysis and hypothesis testing to identify ML opportunities is a plus.

Experience: with dashboards/BI (Power BI/Tableau) and experiment tracking (e.g., MLflow) is a plus.

What qualifications will make you successful for this role?

Educational Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related quantitative field.

Preferred Certifications:

AWS Certified Machine Learning – Specialty AWS Certified Data Analytics – Specialty (or equivalent) Databricks Machine Learning Professional and/or Databricks Generative AI Engineer (plus) Certified Artificial Intelligence Practitioner (CAIP) or similar GenAI/Responsible AI certifications Let us learn about you!

Apply today.

You must submit an online application to be considered for any position with us.

This position will be posted until filled.

Looking to make an IMPACT with your career?

When you are thinking about joining a new team, culture matters.

At Schneider Electric, our values and behaviors are the foundation for creating a great culture to support business success.

We believe that our IMPACT values – Inclusion, Mastery, Purpose, Action, Curiosity, Teamwork – starts with us. IMPACT is also your invitation to join Schneider Electric where you can contribute to turning sustainability ambition into actions, no matter what role you play.

It is a call to connect your career with the ambition of achieving a more resilient, efficient, and sustainable world.

We are looking for IMPACT Makers; exceptional people who turn sustainability ambitions into actions at the intersection of automation, electrification, and digitization.

We celebrate IMPACT Makers and believe everyone has the potential to be one.

Become an IMPACT Maker with Schneider Electric – apply today! €36 billion global revenue +13% organic growth 150 000+ employees in 100+ countries #1 on the Global 100 World’s most sustainable corporations You must submit an online application to be considered for any position with us.

This position will be posted until filled.

Schneider Electric aspires to be the most inclusive and caring company in the world, by providing equitable opportunities to everyone, everywhere, and ensuring all employees feel uniquely valued and safe to contribute their best.

We mirror the diversity of the communities in which we operate, and ‘inclusion’ is one of our core values.

We believe our differences make us stronger as a company and as individuals and we are committed to championing inclusivity in everything we do.

At Schneider Electric, we uphold the highest standards of ethics and compliance, and we believe that trust is a foundational value.

Our Trust Charter is our Code of Conduct and demonstrates our commitment to ethics, safety, sustainability, quality and cybersecurity, underpinning every aspect of our business and our willingness to behave and respond respectfully and in good faith to all our stakeholders.

You can find out more about our Trust Charter here Schneider Electric is an Equal Opportunity Employer.

It is our policy to provide equal employment and advancement opportunities in the areas of recruiting, hiring, training, transferring, and promoting all qualified individuals regardless of race, religion, color, gender, disability, national origin, ancestry, age, military status, sexual orientation, marital status, or any other legally protected characteristic or conduct.

What will you do?

Collaborate with the AI Product Owner to understand the business requirements and define appropriate modelling approaches, experimentation plans, and success metrics.

Coordinate with business teams to monitor model outcomes, gather feedback, and refine/improve machine learning models based on performance insights.

Lead data discovery, feature engineering, experimentation, offline/online evaluation, and productionization with CI/CD for ML; own model documentation, reproducibility, and traceability.

Apply supervised/unsupervised/deep learning, NLP, and LLM techniques (including RAG pipelines, prompt engineering, vector search, and safety guardrails) where they create clear value.

Design and execute rigorous evaluation strategies for ML and GenAI models, including offline metrics, human‑in‑the‑loop reviews for GenAI outputs, regression checks, and failure mode analysis.

Implement governance frameworks for AI models – applying bias/fairness checks, safety filters, responsible AI controls, and executing evaluation protocols defined by Business.

Collaborate with data/ML engineers to industrialize models via APIs/batch jobs, feature stores, scalable serving, and monitoring for drift, performance, cost, and latency.

Lead data mining, collection, and quality initiatives across structured, semi‑structured, and unstructured data to ensure integrity, lineage, and compliance.

Maintain rigorous experiment tracking using tools, ensuring reproducibility and clear lineage across model iterations and experiments.

Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown) Mentor and lead data scientists, conduct design/code reviews, and cultivate best practices in experimentation, evaluation, and documentation.

Track emerging tools/techniques in ML/GenAI and drive reusable frameworks, templates, and SDK/API‑based accelerators to industrialize solutions across the organization.

What skills and capabilities will make you successful?

Technical Experience:

5–8 years of hands-on experience across classical ML (tree‑based methods, GLMs), deep learning (Py Torch/Tensor Flow), and NLP/LLMs (tokenization, embeddings, fine‑tuning, instruction‑tuning, RAG).

Hands‑on with evaluation and safety/guardrail patterns for production GenAI.

Familiarity with ML lifecycle platforms (such as Sage Maker, Azure ML, or Databricks) to run experiments, track models, and provide well‑structured model artifacts to ML Engineers for deployment Comfortable with AWS services for data/ML (e.g., S3, Glue, EMR/Spark, Lambda, SageMaker; Databricks), and integrating with enterprise data lakes/warehouses.

Proficient in Python and ML/DS libraries (Pandas, scikit‑learn, Py Torch/Tensor Flow, XGBoost/LightGBM); strong software practices (testing, linting, packaging).

Strong SQL and data wrangling; experience with Spark/Databricks for large‑scale feature pipelines and training.

Working knowledge of data privacy, safe model behaviors, prompt filtering/output moderation, and auditability for regulated environments.

Exploratory data analysis and hypothesis testing to identify ML opportunities is a plus.

Experience: with dashboards/BI (Power BI/Tableau) and experiment tracking (e.g., MLflow) is a plus.

What qualifications will make you successful for this role?

Educational Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related quantitative field.

Preferred Certifications:

AWS Certified Machine Learning – Specialty AWS Certified Data Analytics – Specialty (or equivalent) Databricks Machine Learning Professional and/or Databricks Generative AI Engineer (plus) Certified Artificial Intelligence Practitioner (CAIP) or similar GenAI/Responsible AI certifications Let us learn about you!

Apply today.

You must submit an online application to be considered for any position with us.

This position will be posted until filled.