Senior Data Scientist, Cross-Domain AI

Position Overview

We are seeking a Senior Data Scientist, Cross-Domain AI to drive advanced analytics and predictive modeling across a complex, data‑rich environment supporting AI‑enabled and autonomous systems. This role focuses on transforming large, heterogeneous datasets into actionable insights and production‑quality models that inform both machine learning development and executive decision‑making.

The ideal candidate combines rigorous statistical methodology with strong engineering discipline, building robust analytical pipelines and continuously improving models through feedback‑driven training and operational data. 

Key Responsibilities

• Perform exploratory data analysis, data cleaning, and data preparation across diverse and complex datasets
• Transform raw, messy, and heterogeneous data (e.g., sensor, telemetry, operational, and production data) into analysis‑ready and ML‑ready formats
• Build predictive and statistical models, including physics‑informed and constraint‑based machine learning approaches
• Design feature engineering frameworks and state‑space representations for reinforcement learning and optimization use cases
• Develop recursive and online training pipelines that continuously retrain and refine models as new data becomes available
• Apply time‑series analysis techniques to identify trends, anomalies, and leading indicators in operational data
• Implement MLOps best practices, including experiment tracking, model versioning, automated validation, and production monitoring
• Build production‑quality analytical pipelines using Python and SQL with robust validation and testing
• Develop causal and statistical models linking process inputs to performance outcomes
• Create analytics, dashboards, and visualizations that translate complex findings into executive‑ready insights
• Collaborate cross‑functionally with engineering, operations, and product teams to support data‑driven decision‑making 

Qualifications

Education

• Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Physics, Engineering, or a related quantitative field
• Equivalent practical experience will be considered 

Experience

• A minimum of 6 years of professional data science experience
• Demonstrated experience deploying production‑quality analytical or machine learning models 

Preferred Qualifications

• Experience working with industrial, scientific, or operational datasets
• Background in advanced modeling techniques such as reinforcement learning, causal inference, or uncertainty quantification
• Experience supporting analytics across complex systems or multi‑disciplinary environments
• Familiarity with recursive, online, or continuous learning workflows
• Advanced academic training or published applied research in a quantitative field 

Soft Skills

• Strong interpersonal and collaboration skills across cross‑functional teams
• Excellent written and verbal communication skills
• Ability to clearly present complex analytical findings to technical and non‑technical stakeholders
• Meticulous attention to detail to ensure accuracy and reproducibility of models and analyses
• Strong problem‑solving skills and intellectual curiosity
• Ability to operate effectively in fast‑paced, ambiguous environments 

Technology Stack

• Python data science and ML ecosystem (e.g., pandas, NumPy, scikit‑learn, PyTorch or TensorFlow)
• Statistical and probabilistic modeling tools (e.g., statsmodels, Bayesian frameworks)
• SQL and data warehousing technologies
• MLOps and experiment tracking tools (e.g., model registries, monitoring, CI/CD for ML)
• Data visualization and dashboarding tools 

Physical Requirements

• Ability to work in a standard office or remote environment
• Ability to use a computer for extended periods
• Ability to participate in meetings and collaborative working sessions

This job description may not be inclusive to the duties and responsibilities listed. Additional tasks may be assigned to the employee from time to time or the scope of the job may change as needed by business demands.