Overview
Roles & Responsibilities
Key Responsibilities
- Design, train, and evaluate supervised & unsupervised models (regression, classification, clustering, uplift).
- Apply automated hyper‑parameter optimization (Optuna, HyperOpt) and interpretability techniques (SHAP, LIME).
- Perform deep exploratory data analysis (EDA) to uncover patterns & anomalies.
- Engineer predictive features from structured, semi‑structured, and unstructured data; manage feature stores (Feast).
- Ensure data quality through rigorous validation and automated checks.
- Build hierarchical, intermittent, and multi‑seasonal forecasts for thousands of SKUs.
- Implement traditional (ARIMA, ETS, Prophet) and deep‑learning (RNN/LSTM, Temporal‑Fusion Transformer) approaches.
- Reconcile forecasts across product/category hierarchies; quantify accuracy (MAPE, WAPE) and bias.
- Establish model tracking & registry (MLflow, SageMaker Model Registry).
- Develop CI/CD pipelines for automated retraining, validation, and deployment (Airflow, Kubeflow, GitHub Actions).
- Monitor data & concept drift; trigger re‑tuning or rollback as needed.
- Design and analyze A/B tests, causal inference studies, and Bayesian experiments.
- Provide statistically‑grounded insights and recommendations to stakeholders.
- Translate business objectives into data‑driven solutions; present findings to exec & non‑tech audiences.
- Mentor junior data scientists, review code/notebooks, and champion best practices.
Desired Candidate
Minimum Qualifications
M.S. in Statistics (preferred) or related field such as Applied Mathematics, Computer Science, Data Science.
5+ years building and deploying ML models in production.
Expert‑level proficiency in Python (Pandas, NumPy, SciPy, scikit‑learn), SQL, and Git.
Demonstrated success delivering large‑scale demand‑forecasting or time‑series solutions.
Hands‑on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Airflow) for model tracking and automated retraining.
Solid grounding in statistical inference, hypothesis testing, and experimental design.
Preferred / Nice‑to‑Have
Experience in supply‑chain, retail, or manufacturing domains with high‑granularity SKU data.
Familiarity with distributed data frameworks (Spark, Dask) and cloud data warehouses (BigQuery, Snowflake).
Knowledge of deep‑learning libraries (PyTorch, TensorFlow) and probabilistic programming (PyMC, Stan).
Strong data‑visualization skills (Plotly, Dash, Tableau) for storytelling and insight communication.
Competencies
ABOUT TE CONNECTIVITY
TE Connectivity plc (NYSE: TEL) is a global industrial technology leader creating a safer, sustainable, productive, and connected future. 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, medical technology and more. With more than 85,000 employees, including 9,000 engineers, working alongside customers in approximately 130 countries, TE ensures that EVERY CONNECTION COUNTS.
WHAT TE CONNECTIVITY OFFERS:
We are pleased to offer you an exciting total package that can also be flexibly adapted to changing life situations - the well-being of our employees is our top priority!
- Competitive Salary Package
- Performance-Based Bonus Plans
- Health and Wellness Incentives
- Employee Stock Purchase Program
- Community Outreach Programs / Charity Events
- Employee Resource Group
IMPORTANT NOTICE REGARDING RECRUITMENT FRAUD
TE Connectivity has become aware of fraudulent recruitment activities being conducted by individuals or organizations falsely claiming to represent TE Connectivity. Please be advised that TE Connectivity never requests payment or fees from job applicants at any stage of the recruitment process. All legitimate job openings are posted exclusively on our official careers website at te.com/careers, and all email communications from our recruitment team will come only from actual email addresses ending in @te.com. If you receive any suspicious communications, we strongly advise you not to engage or provide any personal information, and to report the incident to your local authorities.
Across our global sites and business units, we put together packages of benefits that are either supported by TE itself or provided by external service providers. In principle, the benefits offered can vary from site to site.
- 2 - 6 Yrs
- Maharashtra
- Chennai, Tamil Nadu, India
- Chennai, Tamil Nadu, India
- Chennai, Tamil Nadu, India
- Chennai, Tamil Nadu, India
- Chennai, Tamil Nadu, India