Senior Data Scientist I

We are looking for a passionate Senior Data Scientist with strong hands-on expertise in
AI/ML, Generative AI, Computer Vision, LLMs, Agentic AI, and Edge AI to join our team. The
role will focus on identifying, designing, and enabling AI-driven capabilities across
industrial automation platforms, helping drive intelligent decision-making, operational
efficiency, and next-generation smart manufacturing solutions across edge and cloud
environments.
Key Responsibilities
Machine Learning and Model Analysis
• Apply machine learning, statistical, and experimental design techniques to assess
model behavior and performance in industrial and real-time operational
environments.
• Evaluate the impact of training methodologies, industrial data sources
(sensor/PLC/SCADA streams), model architectures, and deployment strategies
across edge and cloud.
• Review and assess third-party or open-source models, runtimes, and tools from
perspectives of safety, robustness, latency, reliability, and end-to-end industrial
integration.
Research and Prototyping
• Drive research, experimentation, and prototyping of advanced AI/ML techniques
tailored for industrial automation use cases across edge and cloud platforms.
General
• Explore innovative approaches in areas such as anomaly detection, predictive
maintenance, vision-based inspection, hallucination detection, explainability, and
industrial AI trustworthiness.
• Lead proof-of-concepts (PoCs) and experimental studies to evaluate readiness of
new AI methods, models, and metrics for industrial deployment.
• Collaborate with engineering teams to transition successful prototypes into
scalable, production-grade industrial solutions.
• Design scalable system architectures for complex AI/LLM-driven industrial
applications, ensuring seamless integration with OT systems, data pipelines, and
enterprise IT systems.
Collaboration, Documentation, and Governance Support
• Collaborate closely with Line-of-Business (LOB), product, and platform teams to
operationalize AI solutions in industrial automation products.
• Guide and support teams in integrating AI models into Edge platforms, ensuring low
latency inference and high reliability.
• Contribute to documentation, governance, and best practices for deployment,
monitoring, and lifecycle management of AI solutions in industrial ecosystems.
AI Evaluation
• Conduct comprehensive evaluations of models including robustness, latency,
explainability, fairness, reliability, and operational safety in industrial settings.
• Develop benchmark datasets (including IoT/industrial datasets), evaluation
frameworks, and automated testing pipelines for consistent model validation.
• Analyze model architectures, industrial data characteristics, and inference
workflows to optimize performance in resource-constrained edge environments.
• Partner with engineering and domain teams to align evaluation metrics with
industrial standards, validation processes, and deployment guardrails.

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a
related quantitative discipline (or equivalent experience).
• Proven experience in developing, validating, and deploying machine learning
models in production or industrial/pre-production environments.
• Strong proficiency in Python with hands-on experience in data engineering, ML
experimentation, and analytics workflows.
• Strong understanding of experimental design, ensemble learning
(bagging/boosting), statistical analysis, time-series modeling, and forecasting using
lag features.
• Expertise in Microsoft Azure ecosystem: Databricks, Blob Storage, AI Foundry,
Container Registry, Azure Functions, and Web Services.
• Hands-on experience with Computer Vision systems, Edge AI deployment, and real
time inference optimization on industrial hardware.
• Experience with containerization and CI/CD tools: Docker, Git, GitHub.
• Knowledge of FastAPI, Flask, Streamlit, or Gradio for building AI-enabled
applications and dashboards.
• Strong foundation in NLP, Time Series Forecasting, Feature Engineering, and
Predictive Modeling.
• Experience with data processing tools: Pandas, NumPy, PySpark; and visualization
tools such as Matplotlib, Power BI, and Plotly.
• Strong written and verbal communication skills with the ability to explain complex
technical concepts to cross-functional teams.
• Experience with LLM/SLM deployment: safetensors, Llama.cpp, ONNX Runtime,
Azure OpenAI, RAG pipelines, and prompt engineering.
• Strong proficiency in AI/ML frameworks: TensorFlow/Keras, PyTorch, Scikit-learn,
LangChain, ONNX, Vector Databases, LangGraph.
Preferred Qualifications
• Hands-on experience implementing AI evaluation techniques such as grounding
validation, explainability methods, and model risk assessment.

Bring your data science expertise to a team that's ready to support your growth - apply today!

Rewards designed for you

Our Total Rewards is our way of saying: We see you and we value you. It's more than just pay and benefits-it's a meaningful investment in you. It is designed to help you perform, grow, feel safe, and elevate your potential. The package helps you care for yourself and your family, plan your future, grow your skills and career, collaborate in an inclusive workplace, and contribute to your community. At Schneider Electric, we're here for what matters most to you. Discover more at our Career Page .

* Country-specific programs and initiatives may be available.

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.