STAFF DATA SCIENTIST, AI/ML SOFTWARE ENGINEERING (#, CA, US, #)

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 The pace of innovation in artificial intelligence, machine learning, and advanced analytics 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 applying data science and analytics to drive insights, improve engineering productivity, and enable next-generation products. This role is a strong fit for someone with a solid analytical and statistical foundation who is eager to apply data science techniques to real engineering and business problems. You will work closely with engineers, machine learning engineers, and cross-functional teams to analyze data, develop predictive models, and generate insights that support product development and operational decision-making. What you will be doing As a staff Data Scientist, you will analyze complex datasets and develop statistical and machine learning models that help uncover insights and drive data driven decisions. You will contribute to data exploration, model development, and analytical workflows while collaborating with engineering and product teams to translate data into actionable recommendations. Key Responsibilities Advanced Data Collection and Analytics Lead the development of advanced statistical and machine learning models addressing complex engineering and business challenges. Identify strategic opportunities where data science can improve engineering productivity, product design, or operational performance. Define analytical approaches and modeling frameworks used across multiple projects. Data Architecture and Analytical Platforms Guide development of scalable data pipelines and analytical frameworks that support enterprise data science initiatives. Define best practices for data collection, feature engineering, model evaluation, and reproducibility. Support integration of advanced analytics and machine learning models into production systems. Technical Leadership Serve as a technical advisor on complex analytical challenges across engineering and product teams. Mentor and guide junior data scientists and analysts. Influence analytical standards, tools, and methodologies across the organization. Communication and Business Impact Translate complex analytical findings into strategic recommendations for engineering leadership and business stakeholders. Develop advanced visualizations and dashboards to support decision making. Partner with cross functional teams to ensure analytical insights are implemented in real world systems. What your background should look like Bachelor’s with 8+ years, Master’s with 6+ years, or PhD with 3+ years of experience in Computer Science, Data Science, Statistics, Mathematics, Electrical Engineering, or a related technical discipline. Strong experience in statistical modeling, machine learning, and data analysis. Strong programming skills in Python, R, or similar analytical programming languages. Experience working with large datasets, data science libraries, and complex analytical problems. Experience using visualization tools such as Tableau or Power BI. Strong communication and data storytelling skills. Ability to work independently on analytical projects and influence technical decisions. Excellent presentation and stakeholder management skills. Ability to travel 10% to 25% to customers, conferences, partner locations, and TE development sites. #LI-REMOTE Competencies Values: Integrity, Accountability, Inclusion, Innovation, Teamwork COMPENSATION Competitive base salary commensurate with experience: $154,100 – $231,100 (subject to change dependent on physical location) Posted salary ranges are made in good faith. TE Connectivity reserves the right to adjust ranges depending on the experience/qualification of the selected candidate as well as internal and external equity. Total Compensation = Base Salary + Incentive(s) + Benefits