Director of Data and AI Solutions

TRC's client, a leading EV battery manufacturer backed by major industry partners, is seeking an IT Director of Data & AI to lead data strategy, AI adoption, and digital transformation across the enterprise. This is a high-impact leadership role with significant growth opportunities.

Key Responsibilities :

Data Architecture & Ecosystem - Design secure, scalable architectures enabling real-time analytics, integration, and data flow across the organization.

AI Strategy & Solutions - Define enterprise AI strategy and deliver AI / ML solutions that improve manufacturing (predictive maintenance, quality assurance, process optimization, yield improvement).

Integration & Governance - Connect diverse data sources (IoT sensors, manufacturing equipment, SAP ERP, supply chain platforms) while ensuring data quality, privacy, and compliance (ISO, GDPR).

Technology Leadership - Evaluate and implement emerging AI, IoT, big data, and cloud technologies (AWS, Azure, GCP). Establish AI governance frameworks and best practices for MLOps.

Collaboration & Leadership - Partner with IT, engineering, and operations teams to translate business needs into scalable AI-powered solutions. Mentor data scientists, engineers, and analysts to foster innovation.

Qualifications :

Bachelor's in CS, Data Science, Engineering, or related field; Master's preferred.

10+ years in IT, with 7+ in data architecture, data engineering, or AI solution development (manufacturing / industrial experience preferred).

Expertise in SQL / NoSQL, ETL, and AI / ML frameworks (TensorFlow, PyTorch, Scikit-learn).

Experience with big data platforms (Databricks, Spark, Hadoop) and IoT / time-series data.

Strong cloud expertise (AWS, Azure, GCP) and programming skills (Python, Java, or Scala).

Proven success implementing AI-driven solutions for process optimization or predictive maintenance.

Excellent communication and leadership skills; able to influence technical and non-technical stakeholders.

Preferred Skills :

EV, automotive, or battery manufacturing background.

Knowledge of materials science data, battery chemistry, or electrochemical modeling.

Experience with digital twins, simulation platforms, and CI / CD pipelines for AI / ML models.

Certifications in cloud architecture, data engineering, or machine learning.