Lead Data Science @ TE Connectivity

Technical Strategy & Solution Ownership

• Define and drive the technical direction for ML, Deep Learning, LLM, and Generative AI initiatives aligned with business goals.
• Own end-to-end solution design decisions, including architecture, modeling approach, and deployment strategy.
• Evaluate emerging AI technologies and recommend pragmatic adoption based on feasibility, scalability, risk, and ROI.
• Act as a technical authority on trade??offs between model complexity, performance, cost, and interpretability.

Advanced Modeling & Applied AI

• Design, build, evaluate, and deploy:
• Supervised and unsupervised ML models
• Deep learning models (CNNs, RNNs, Transformers, transfer learning)
• NLP and LLM??based solutions (RAG, embeddings, GenAI workflows, agent-based systems where applicable)
• Apply strong fundamentals in statistics, experimentation, and validation to ensure robustness and reliability.
• Demonstrate judgment in choosing simple vs. complex approaches based on business context.

End-to-End ML & MLOps

• Architect and implement production-grade ML pipelines, including:
• Data ingestion and preprocessing
• Feature engineering and reuse
• Model training, validation, deployment, and serving
• Monitoring, drift detection, and automated retraining
• Partner with Data Engineering and Platform teams to build scalable, cloud??native ML systems (AWS / Azure / GCP).
• Ensure best practices around model versioning, observability, lineage, and reproducibility.
• Adhere to data governance, security, privacy, and compliance standards.

Data Modeling & Data Architecture

• Design and review logical and physical data models to support analytics and ML workloads.
• Influence data architecture decisions (e.g., lakehouse, feature stores, semantic layers) to ensure:
• Data quality
• Performance
• Reusability
• Collaborate closely with Data Engineering teams on schema design and data readiness for ML.

Databricks & Lakehouse Expertise

• Hands??on experience with Databricks and Lakehouse architectures, including:
• Delta Lake
• Auto Loader & Pipelines
• Feature Store & Unity Catalog
• MLflow Tracking, Model Registry, and Model Serving
• Optimize ML and data workloads for performance, scalability, and cost efficiency.
• Define best practices for collaborative development using notebooks, repos, and CI/CD workflows.

Application Development & Model Consumption

• Build ML??powered applications and tools to expose insights and models to users and downstream systems.
• Experience developing applications using:
• Django / FastAPI or similar Python frameworks
• Streamlit / Dash for rapid analytical interfaces
• Design and implement REST APIs for model inference and integration.
• Partner with Engineering teams to ensure applications meet performance, security, and deployment standards.

EDUCATION/KNOWLEDGE

Masters in computer science, Data Science, Machine Learning, Statistics, or related field.

Required Qualifications

Experience & Background

• 12-15 years of experience in Data Science, Applied ML, or AI-driven product development.
• Proven track record of owning largescale, business-critical ML/AI solutions.
• Experience working in environments with high ambiguity and cross-functional dependencies.

Technical Skills

• Strong expertise in:
  • Machine learning and statistical modeling
  • Deep learning and neural architecture
  • NLP, LLMs, and Generative AI systems
• Proficiency in:
  • Python (mandatory)
  • SQL (mandatory)
  • TensorFlow, PyTorch, and modern ML frameworks
• Experience deploying and operating models in production.

Data & ML Platforms

• Strong hands-on experience with Databricks, including:
• Delta Lake, Feature Store, MLflow
• Model Registry and Model Serving.