Job highlights

- Master's in CS/Data Science with 6+ years in ML/DL, expertise in SAP IBP, time-series modeling, transformers, LLMs, and MLOps
- Define technical roadmap and architect AI and SAP IBP solutions for demand forecasting, lead AI modeling and deployment, ensure governance and explainability, integrate AI with SAP IBP workflows, and drive adoption and business value

At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world.

Job Overview

- Define the technical roadmap for Global Demand Management AI initiatives and SAP IBP capabilities.
- Architect end-to-end AI and SAP IBP solutions supporting demand forecasting, data integration, model deployment, and monitoring.
- Provide technical leadership across demand analytics, AI modeling, experimentation, and SAP IBP solution development.
- Establish standards for AI and SAP IBP governance, explainability, reproducibility, documentation, and sustainable adoption.
- Design and deploy machine learning models for demand forecasting, segmentation, anomaly detection, and predictive insights within SAP IBP.
- Apply feature engineering, model optimization, and MLOps practices to deliver scalable AI solutions supporting Global Demand Management and SAP IBP.
- Evaluate model performance and business impact to improve forecast accuracy, exception management, and demand-planning decisions.
- SAP IBP integration: Connect AI solutions with SAP IBP data, workflows, analytics, and planning processes.
- Demand-specific use cases: Forecast explanations, exception prioritization, demand-driver analysis, scenario recommendations, and planner support.
- Human-in-the-loop controls: Position AI as decision support rather than fully autonomous decision-making.
- Time-series expertise: Retain transformers, RNNs/LSTMs, embeddings,



and transfer learning only where relevant to forecasting and demand patterns.
- Responsible AI: Include explainability, validation, security, governance, and monitoring.
- Adoption and value: Measure improvement in forecast accuracy, planner productivity, exception resolution, and decision quality.

What your background should look like:

- Master's in Computer Science, Data Science, Machine Learning, Statistics, or related field.
- 6+ years of experience in machine learning, deep learning, natural language processing, or applied Robust proficiency in Python, ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn), and data pipelines.
- Hands-on knowledge of SAP IBP for Demand, planning data, forecasting processes, analytics, and workflow integration.
- Strong understanding of demand forecasting, time-series modeling, segmentation, forecast accuracy, bias, exceptions, and scenario analysis.
- Experience with LLMs, Generative AI, Agentic AI, vector databases, RAG pipelines, and model evaluation and fine-tuning.
- Experience with transformers, RNNs/LSTMs, embeddings, transfer learning, and predictive modeling for demand and time-series use cases.
- Familiarity with MLOps, cloud platforms, and big-data ecosystems, including model deployment, monitoring, governance, and security.
- Ability to translate demand-management requirements into scalable AI and SAP IBP solutions and drive stakeholder alignment, adoption, and measurable business value.

Competencies Values: Integrity, Accountability, Inclusion, Innovation, Teamwork

ABOUT TE CONNECTIVITY

TE Connectivity plc (NYSE: TEL) is a global industrial technology leader creating a safer, sustainable, productive, and connected future. As a trusted innovation partner, our broad range of connectivity and sensor solutions enable the distribution of power, signal and data to advance next-generation transportation, energy networks, automated factories, data centers enabling artificial intelligence, and more.

📌 Global Demand Expert & Data Scientist (Bengaluru)
🏢 TE Connectivity
📍 Bengaluru