Interested in this AI/ML Engineer role at TE Connectivity?
Apply Now →About This Role
The Lead Data Science-Pricing role is an AI and advanced analytics position with specialized responsibility for pricing and commercial decision support. The role frames complex business challenges as data, AI and analytical opportunities; develops insights and analytical products; and applies these capabilities to quote pricing, book-price effectiveness, margin improvement, agreement performance, transactional pricing leakage, tariff recovery, customer segmentation, market intelligence, and pricing opportunities. The role partners across Sales, Product Management, Finance, Customer Care, Engineering, IT, and business teams to define pricing strategies and solution roadmaps, automate pricing workflows, guide execution, communicate recommendations, track adoption and measurable value.
Responsibilities
- Gather integrate and validate pricing, customer, product, cost, agreement, transaction, market and quote data from enterprise, ERP, cloud, and external sources.
- Perform exploratory analysis and automate recurring data collection, pricing uploads or downloads, price-request support, and reporting workflows.
- Develop dashboards and visualizations in relevant pricing platforms to provide transparent and actionable insights. r
- Define and execute pricing strategies, governance, KPIs, customer and product playbooks, tariff-recovery actions, and commercial initiatives aligned with business objectives and market dynamics.
- Drive process and system optimization, balancing price improvement and transactional execution while improving book-price effectiveness, agreement performance, low-margin business, pricing leakage, and realized versus expected recovery.
- Design scalable pricing logic, business rules, and decision frameworks that use AI and automation to assess incoming opportunities, recommend competitive prices, improve quote conversion, and support appropriate human review.
- Segment industries, customers, and products; apply marketplace, competitor, economic, supply-chain, volume, and customer-sensitivity insights to pricing decisions.
- Analyze quote and opportunity demand signals, winning prices, customer behavior, product mix, competitive price movements, and distributor or external market data to identify pricing opportunities.
- Balance short-term margin improvement with long-term customer and commercial strategy; track adoption, value generation, conversion, pricing outcomes, and tariff-recovery performance.
- Design advanced analytics and AI algorithms, conversational AI, and Generative AI solutions for pricing and commercial use cases such as book price effectiveness, low margin business, tariff recovery & price leakage.
- Use experimentation, model-performance measures, and continuous monitoring to compare approaches, validate hypotheses, identify risks and opportunities, and refine recommendations.
- Partner with engineering, and IT teams to productionize, integrate, scale, and monitor analytical models and AI-enabled pricing solution commercial decision support. The role frames complex business challenges as data, AI and analytical opportunities; develops insights and analytical pro
- Simplify complex analyses and pricing processes through clear presentations, dashboards, written communication, and data-driven storytelling; influence without authority and support fact-based customer negotiation guidance.
- Engage pricing leadership to translate complex pricing and commercial challenges into analytical opportunities with tangible impact on strategic priorities.
- Develop data-driven use cases through rapid prototyping and iterative analysis; prepare business cases and guide proposals through qualification and prioritization.
- Define and execute analytical product roadmaps by evaluating business value, model feasibility, data readiness, and technical dependencies while balancing near-term priorities with long-term strategy.
- Lead requirements gathering and cross-functional alignment with Sales, Product Management, Finance, Customer Care, Engineering, IT, and business-unit stakeholder’s
- Lead requirements gathering and cross-functional alignment with Sales, Product Management, Finance, Customer Care, Engineering, IT, and business-unit stakeholder’s
What your background should look like:
- 5+ years of experience in AI/advanced analytics, pricing analytics, commercial analytics a comparable role supporting complex business decisions.
- Experience with pricing, commercial, customer, product, and related decision processes is preferred,
- Graduate degree (MS or MBA) in Computer Science, Analytics, Statistics, Information Systems, Economics, Finance, Business, or a related quantitative field preferred.
- Equivalent combination of relevant education and experience may be considered book-price effectiveness, margin improvement, agreement performance, transactional.
- Margin achievement, price realization, and quote conversion versus business targets
- Book-price, agreement, pricing, and tariff-recovery performance versus plan
- Reduction of transactional and tariff-related margin leakage
- Accuracy, transparency, timeliness, and adoption of pricing analytics and reporting
- Automation and AI enhancement of pricing workflows, recommendations, monitoring, and exception handling
- Execution of pricing strategies, prioritized roadmaps, playbooks, and continuous-improvement initiatives
- Effective stakeholder alignment, communication, 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.
Our more than 90,000 employees, including 10,000 engineers, work alongside customers in approximately 130 countries. In a world that is racing ahead, TE ensures that EVERY CONNECTION COUNTS. Learn more at www.te.com and on LinkedIn, Facebook, WeChat, Instagram and X (formerly Twitter).
COMPENSATION
- Competitive base salary commensurate with experience: $145,400- $ 218,100 (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
BENEFITS
- A comprehensive benefits package including health insurance, 401(k), disability, life insurance, employee stock purchase plan, paid time off and voluntary benefits.
EOE, Including Disability/Vets
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.
Job Locations:
3800 REIDSVILLE ROAD
WINSTON SALEM, North Carolina 27101-2166
United States
Posting City: WINSTON SALEM Job Country: United States Travel Required: Less than 10% Requisition ID: 160066 Workplace Type: Remote External Careers Page: Information Technology
Salary Context
This $145K-$218K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At TE Connectivity, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($181K) sits 14% below the category median. Disclosed range: $145K to $218K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
TE Connectivity AI Hiring
TE Connectivity has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Winston-Salem, NC, US. Compensation range: $218K - $218K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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