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About This Role
Marketing Data Science Advisor
The individual will provide expertise and leadership in handling vast amounts of data, enterprise analytics, statistical modeling, and AI/ML initiatives. They will utilize their knowledge of customer data, market dynamics, and internal business processes to develop analytical solutions for complex business problems.
Essential Functions
- Creates and executes analytical solutions from concept to production, communicating results to diverse audiences, and providing thought leadership in advanced modeling and analysis.
- Provides thought leadership and leads cross\-functional teams in advanced descriptive, diagnostic, predictive and prescriptive modeling, advanced statistical analysis, and other quantitative analysis of complex business situations.
- Works on multiple complex assignments concurrently.
- Makes advanced use of current and emerging technologies and advanced use of data, statistical and quantitative analysis, explanatory and high\-complexity predictive modeling, and fact\-based management to evaluate trends and develop actionable insights and to drive recommendations to business partners and management.
- Provides thought leadership and leads cross\-functional teams in advanced descriptive, diagnostic, predictive and prescriptive modeling, advanced statistical analysis, and other quantitative analysis of complex business situations.
- Provides consultation to management on a regular basis and formally prepares and presents recommendations to leadership.
- Mentors less senior staff.
- Perform other duties as assigned.
Preferred Qualifications
- Advanced descriptive, diagnostic, predictive and prescriptive modeling knowledge and skills.
- Advanced data science and statistical analysis knowledge, and other quantitative analysis of complex business situations.
- Ability to communicate highly complex information in an understandable manner to both technical and non\-technical audiences at all levels.
- Strong cross functional collaboration and project leadership skills.
Minimum Education
Masters degree in data science, analytics, business, mathematics, economics, computer science or other quantitative fields such as engineering/operations research. Directly related advanced degrees may offset experience requirements.
Minimum Experience
Three (3\) years work experience in data science, analytics, business, mathematics, economics, computer science, or other quantitative fields such as engineering/operations research, in an analytical, quantitative, or technical role required. Two (2\) of those years of experience or coursework should be with the following skills (skills can be gained concurrently with education): computer\-aided decision support (utilize computer programs or packages to analyze data and support business decisions), or any analytical/modeling language (e.g. Python, R, SAS, or SQL), and data visualization tools like Tableau, Power BI, Spotfire, etc. will be considered equivalent.
Knowledge, Skills and Abilities
- Advanced descriptive, diagnostic, predictive and prescriptive modeling knowledge and skills.
- Strong problem\-solving abilities.
- Analytical/modeling language knowledge.
- Advanced statistical analysis knowledge, and other quantitative analysis of complex business situations. Ability to communicate highly complex information in an understandable manner to both technical and non\-technical audiences at all levels.
- Strong collaboration and project leadership skills.
- Ability to mentor lower levels
Sr. Marketing Data Scientist
- Under moderate supervision provides expertise at the enterprise level related to vast quantities of data (including disparate data), enterprise analytics, statistical modeling, and artificial intelligence (AI)/machine learning (ML) initiatives.
- Uses expert knowledge of data on customers, consumers, economy, external demand drivers, market, and competition (structured and unstructured) and internal business processes to develop analytical solutions to relatively well\-defined and lower complexity business problems.
- Creates and executes solutions from initial concept to fully tested production and communicates results to a broad range of audiences.
- Provides thought leadership and leads cross\-functional teams in descriptive, diagnostic, predictive and prescriptive modeling, advanced statistical analysis, and other quantitative analysis of complex business situations.
- Makes use of current and emerging technologies to evaluate trends and develop actionable insights and recommendations to business partners and management.
- Makes use of data, statistical and quantitative analysis, explanatory and high\-complexity predictive modeling, and fact\-based management to drive decision\-making.
- Leads cross functional projects and programs, formally preparing and presenting to management.
- Provides consultation to management on a regular basis.
- Routinely works on multiple assignments concurrently.
- Mentors less experienced staff.
Preferred Qualifications
- Advanced descriptive, diagnostic, predictive and prescriptive modeling knowledge and skills.
- Advanced data science and statistical analysis knowledge, and other quantitative analysis of complex business situations.
- Ability to communicate highly complex information in an understandable manner to both technical and non\-technical audiences at all levels.
- Strong cross functional collaboration and project leadership skills.
Minimum Education
Master's degree in data science, analytics, business, mathematics, economics, computer science or other quantitative fields such as engineering/operations research. Related experience may offset degree requirements and related education/degree may offset experience requirements.
Minimum Experience
Two (2\) years work experience in business, mathematics, economics, computer science, or other quantitative fields such as engineering/operations research, in an analytical, quantitative, or technical role. One (1\) year of experience or coursework with the following skills (skills can be gained concurrently with education): computer\-aided decision support (utilize computer programs or packages to analyze data and support business decisions), or any analytical/modeling language (e.g. Python, R, SAS, or SQL), and data visualization tools like Tableau, Power BI, Spotfire, etc.
Knowledge, Skills and Abilities
- Strong problem solving, analytical, and communication skills.
- Directly related advanced degrees may offset experience requirements.
Domicile
This is a hybrid position located in Memphis, TN, Plano, TX or Pittsburgh, PA. Candidates must live within 50 miles of the campus location. Employees will be required to work at the FedEx campus location several times per week.
Preferred Qualifications:
Pay Transparency:
Pay:
Additional Details: Posting closes on: 08/11/2026 at 4:00 PM CST / 5:00 PM EST.
For details on our comprehensive benefits, click here.
Federal Express Corporation is an Equal Opportunity Employer including, Vets/Disability.
Reasonable accommodations are available for qualified individuals with disabilities throughout the application process. Applicants who require reasonable accommodations in the application or hiring process should contact [email protected].
Applicants have rights under Federal Employment Laws:
- Know Your Rights
- Pay Transparency
- Family and Medical Leave Act (FMLA)
- Employee Polygraph Protection Act
E\-Verify Program Participant: Federal Express Corporation participates in the Department of Homeland Security U.S. Citizenship and Immigration Services’ E\-Verify program (For U.S. applicants and employees only). Please click below to learn more about the E\-Verify program:
- E\-Verify Notice (bilingual)
- Right to Work Notice (English) / (Spanish)
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At FedEx, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
FedEx AI Hiring
FedEx has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Coraopolis, PA, US, Collierville, TN, US, Plano, TX, US. Compensation range: $162K - $217K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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