The AI & Data Science Lead is responsible for designing and deploying AI and data-science solutions that tightly couple testing, simulation, design, and optimization workflows while operating under documented, auditable regulated processes. They develop and productionize models and analysis pipelines on top of experimental and simulation data, embed AI capabilities into core engineering tools, and stand up governed environments where engineering teams can use these capabilities safely and repeatably, thus shortening test cycles and expanding design-space exploration without compromising compliance, quality, or safety obligations.
Responsibilities
Defines and maintains an integrated AI and data-science program framework for testing, simulation, and optimization groups that is fully embedded in the organization’s regulated workflows, quality system, and change-control processes.
Integrate AI and data-science solutions into engineering toolchains (CAD/CAE/CFD, PLM, requirements/test management, experiment databases, optimization frameworks) with full traceability of data, models, and decisions for audit and regulatory review.
Ensure all AI and analytics solutions follow documented lifecycle procedures (requirements, validation and verification, approvals, deployment, monitoring, and retirement) that meet applicable regulatory, safety, and quality standards.
Design and operate controlled AI and analytics workspaces (notebooks, design copilots, code assistants, data-access layers) where access, logging, and approvals align with classification, export-control, and regulatory rules.
Partner with quality, safety, cyber, and legal/compliance functions to define, implement, and continuously refine regulated workflows for AI and data-science use, including model risk assessments and sign-off criteria.
Coordinate and document incident response and model review when AI or analytics behavior may affect safety, quality, or regulatory posture, ensuring corrective actions are captured in the formal quality and regulatory systems.
Requirements
Experience applying data-science and ML in regulated or safety-critical environments (e.g., nuclear, aerospace, medical, energy) with familiarity in how digital tools are controlled under formal quality and regulatory frameworks.
Ability to design “safe by default” AI and analytics workflows, including access control, approval steps, and monitoring, so that regulated constraints are built into everyday tools and processes rather than handled ad hoc.
Strong applied statistics and data-analysis skills (e.g., regression, design of experiments, uncertainty quantification, time-series analysis) relevant to simulation and test data.
Demonstrated experience analyzing complex engineering or scientific datasets (e.g., CFD/FEA outputs, rig test data, sensor time series) and translating results into design or test decisions.
Proficiency with data-analysis environments and tools (e.g., Python scientific stack, Jupyter, SQL, basic BI/visualization tools) and their integration with engineering data sources.
Demonstrated ability to follow detailed procedures and instructions accurately, particularly in safety-critical environments.
Strong analytical mindset, problem-solving attitude, and communication skills, with the ability to work in a collaborative, multidisciplinary environment.
Degree in Data Science, Computer Science, Engineering, Physics, or a related quantitative field; an advanced degree is preferred but not required if experience is strong.
Solid grounding in statistics and machine learning, with demonstrated ability to apply these tools to engineering or scientific data (tests, simulations, sensor data).
Familiarity with engineering environments (e.g., simulation, test rigs, optimization workflows) and comfort working directly with engineers and scientists.
Proven ability to work within or alongside regulated or safety-critical programs, following documented procedures and quality processes.
3+ years applying data-science and AI techniques to engineering, physical-science, or R&D problems (e.g., modeling test outcomes, building surrogates for simulations, optimization support).
Demonstrated ownership of end-to-end projects: from problem framing and data preparation through model development, validation, deployment, and hand-off to users.
Experience collaborating with cross-functional teams (engineering, testing, simulation, operations, IT, quality/compliance) and influencing technical direction without formal authority.
Evidence of operations in structured or regulated workflows (e.g., change control, documented analyses, formal reviews) and maintaining high standards for documentation and reproducibility.