Job Overview

The pace of innovation in artificial intelligence and machine learning continues to accelerate, and the Data and Devices Business Unit is at the center of this transformation. Our customers are building intelligent systems that redefine how data is processed, analyzed, and used across compute, networking, and cloud infrastructure. At TE Connectivity, we support this evolution by delivering scalable, high-quality software solutions that enable next-generation products. 

This role is a strong fit for someone with a solid software and analytical foundation who is eager to apply machine learning techniques to real engineering problems. You will work closely with experienced engineers, data scientists, and cross functional teams to develop models, improve workflows, and support the integration of ML capabilities into our tools and processes. 

Job Responsibilities

Data Collection and Preprocessing

Collect and process structured and unstructured datasets from engineering systems, databases, and operational tools.
Clean and validate datasets to ensure accuracy and consistency.
Develop scripts and pipelines for data preprocessing and transformation.

 
Exploratory Data Analysis
Perform exploratory analysis to identify patterns, correlations, and insights within datasets.
Investigate data quality issues and anomalies that may impact model development.

 
Machine Learning Model Development

Implement machine learning algorithms such as regression, classification, clustering, and anomaly detection.
Support feature engineering and model training workflows.
Evaluate model performance using statistical and machine learning metrics.

Model Deployment and Integration

Assist in deploying machine learning models into production systems.
Support integration of models into engineering tools, APIs, and data pipelines.
Work with software engineers to ensure models operate reliably in production environments.

 
Performance Monitoring

Monitor deployed models for accuracy and performance degradation.
Support retraining and model updates when data drift occurs.

Required Skills & Competency

Bachelor’s with 4+ years, Master’s with 3 years, or PhD with 1 years of experience in Computer Science, Data Science, Software Engineering, Electrical Engineering, or a related technical discipline.
Programming experience in R or Python
Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit learn
Experience working with data analysis libraries such as pandas or NumPy
Familiarity with data pipelines, model evaluation, and machine learning experimentation
Knowledge of software development practices, including version control and testing
Strong analytical thinking and problem-solving skills
Ability to collaborate effectively in cross-functional engineering teams
Clear communicator who works well in a collaborative team environment.
Self-motivated and eager to learn new technologies with guidance.
Ability to travel occasionally, up to 10 percent, for team meetings or training as needed.

Competencies