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About This Role
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Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow—people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.
Job Description:
The Customer First Analytics Senior Data Science Manager leads the development, deployment, and operationalization of advanced analytics, machine learning, and AI-driven solutions that enable Sales and Marketing teams to drive revenue growth, improve customer engagement, and enhance customer retention. This role develops and executes a roadmap for high-impact analytical use cases across the Commercial organization, transforming large volumes of structured and unstructured data into predictive and prescriptive insights that drive measurable business outcomes.
The Senior Data Science Manager is responsible for planning, developing, testing, validating, and monitoring analytical models to ensure they meet business needs and align with analytical best practices. Working closely with cross-functional stakeholders, this individual solves complex business challenges, identifies growth opportunities, and translates data-driven insights into strategic recommendations.
In addition, this role provides technical leadership and mentorship to junior data scientists, fostering collaboration, innovation, and professional growth. The position influences department and project teams, drives alignment across stakeholders, and helps advance UPS Commercial Analytics through data-driven decision-making and continuous innovation.
At UPS, you'll have the opportunity to work with talented teams, explore innovative possibilities, and grow your skills in an energetic culture that empowers employees to improve every day. If you have the passion, leadership, and analytical expertise to drive results and inspire others, this role offers an exciting opportunity to take Commercial Analytics to the next level.
Key Responsibilities
- Develops and maintains predictive models that support customer acquisition, retention, churn prevention, cross-sell, upsell, customer lifetime value, and other initiatives
- Partners with Sales and Marketing leaders to identify high-value business opportunities and quantify potential impact
- Designs customer segmentation targeting and personalization efforts
- Applies machine learning, statistical modeling, and experimentation techniques to solve complex business challenges
- Designs and evaluates tests and controlled experiments to measure model effectiveness
- Partners with data engineering teams to develop trusted, scalable, and well-governed data pipelines
- Collaborates closely with cross functional / cross business unit stakeholders to solve key business problems
- Influences strategic decisions through data-driven recommendations
Qualifications
Required
- Must have a master's degree (or international equivalent) or be a current employee with a minimum of three years of UPS experience
- Possesses 5+ years of data science, machine learning, predictive analytics, or advanced analytics roles
- Possesses experience leading projects or managing data scientists and analytics professionals
- Exhibits critical thinking and problem-solving skills
- Demonstrates advanced proficiency in Python, SQL, PySpark, and other analytical programming languages
- Maintains strong knowledge of machine learning, statistical modeling, forecasting, clustering, large language models, optimization, and experimental design
- Demonstrates strong analytical, problem-solving, and quantitative modeling skills
- Displays strong understanding of UPS data systems and sources
- Possesses excellent written, visual, and verbal communication skills with the ability to present insights to both technical and non-technical audiences
- Demonstrates proven ability to manage multiple priorities and influence cross-functional stakeholders
Preferred
- Master’s degree in Statistics, Data Analytics, or a related field
- Demonstrates proficiency in R
Additional Information
- This job is an internal grade 30F
- The last day to apply for this role is October 19, 2026 (11:59 PM EST)
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $108,720.00/year in our lowest geographic market up to $201,540.00/per year in our highest geographic market. Pay is based on several factors including, but not limited to market location, and may vary depending on the job-related knowledge, skills, and education/training and a candidate’s work experience. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors, including, but not limited to, individual performance, business unit performance, and/or the company’s performance. Hired applicants may be eligible for medical, dental & vision benefits, Employee Assistance Program, personal/sick paid time, Educational Assistance Program, 401(k) Retirement Savings Plan, vacation, basic life and voluntary group life insurance programs, Health Savings, Flexible Spending and Dependent Care accounts, Discounted Employee Stock Purchase Plan, Adoption Assistance Benefit, and paid holidays.
This position is eligible for a bonus based on company performance.
Employee Type:
Permanent
UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.
Employer will sponsor visas for specific positions. UPS is an equal opportunity employer. UPS does not discriminate on the basis of race/color/religion/sex/national origin/veteran/disability/age/sexual orientation/gender identity or any other characteristic protected by law.
Salary Context
This $108K-$201K range is below 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 UPS, 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 Required
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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $108K to $201K.
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.
UPS AI Hiring
UPS has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, MLOps Engineer. Positions span Chennai, Tamil Nadu, India, Mahwah, NJ, US, Louisville, KY. Compensation range: $201K - $201K.
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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