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 guar...