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About This Role
Data Scientist I (GCP \& AI Engineer)
Overview
Ignite your career as an early\-career Data Scientist, playing a pivotal role in designing, building, and deploying innovative solutions that deliver tangible business value. We are seeking individuals who are profoundly passionate not only about developing cutting\-edge models but also about constructing the feature preparation systems and deployment workflows essential for their success. This includes learning to leverage APIs for model integration and actively exploring the transformative potential of Generative AI. You will contribute to our mission within a collaborative, mentorship\-driven environment, working hands\-on with the Google Cloud Platform (GCP) ecosystem under the guidance of senior team members.
Works as part of a team to employ scientific methods and data\-discovery tools to find new patterns, insights, and relationships in big data; extract meaningful information from multiple large data sets and develop solutions that quickly and visually communicate results. Participates and collaborates in discussions across business and IT teams to understand data product needs. Analyzes data from various databases to drive optimization and improvement of product development and business strategies. Independently works on analyzing and interpreting data to identify trends, insights, and logical data driven solutions for business problems. Develops analytical models and algorithms which apply data to identify business improvement insights and inform the development of processes and tools (e.g. dashboards, other visualizations) for monitoring, analyzing, and evaluating analytical model performance. Ensures accuracy and quality of data, including reconciliation from disparate sources. Partners with other team members to collaborate with stakeholders, customers, and other functional teams. Works alongside other data scientists to understand business questions, identify opportunities to leverage data to drive business solutions, implement models, and monitor outcomes.
Responsibilities
- Foundational Modeling: Develop and implement predictive and descriptive analytics on structured and unstructured data, encompassing classification, regression, clustering, and hypothesis testing.
- Data Preparation \& Feature Engineering: Clean, curate, and transform raw data from existing tables and systems using SQL and Python to prepare high\-quality datasets for model training and evaluation.
- Data Retrieval \& Manipulation: Write efficient SQL queries and Python scripts to extract, manipulate, and explore data housed within BigQuery and Cloud Storage (GCS).
- Business Intelligence: Design and create intuitive dashboards and visualizations in Looker and Looker Studio to communicate metrics and insights clearly to team members and stakeholders.
- MLOps \& Version Control: Actively participate in code reviews, enforce version control (Git), and apply foundational CI/CD and MLOps practices (such as model tracking) for seamless deployment.
- GenAI Exploration: Explore Generative AI concepts and foundational models using Vertex AI Generative AI Studio to identify potential business use cases.
- Collaboration \& Domain Growth: Collaborate with business partners to translate operational questions into analytical insights, while cultivating deep domain expertise in FedEx data systems and business operations.
Minimum Qualifications
- Education: Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Industrial Engineering, or a closely related quantitative field.
- Experience: 0–3 years of relevant experience (internships, academic projects, or co\-ops are highly valued).
- Communication: Strong communication skills, an innate eagerness to learn, and a proactive, collaborative problem\-solving mindset.
Required Technical Skills
- Core Languages: Proficiency in SQL and Python.
- ML Frameworks: Foundational knowledge of machine learning libraries (scikit\-learn, XGBoost, TensorFlow, or PyTorch).
- GCP Analytics Services: Hands\-on exposure to Google Cloud Platform (GCP), specifically BigQuery, Vertex AI, and Cloud Storage (GCS) for data retrieval and modeling.
- API Integration: Demonstrated ability to interact with and leverage RESTful APIs for consuming model inputs and integration.
- Data Visualization: Experience with at least one visualization tool (e.g., Looker, Looker Studio, Tableau) or Python plotting libraries (Matplotlib, Seaborn).
- Version Control \& DevOps: Foundational understanding of Git, relational database concepts, and basic MLOps/DevOps workflows.
Preferred Qualifications
- GCP Fundamentals: Familiarity with GCP cloud environments (e.g., passing the Google Cloud Digital Leader or Associate Cloud Engineer exam is a plus).
- Advanced GenAI: Academic or personal project experience with Large Language Models (LLMs) or prompt engineering.
- Industry Context: Internship or project experience in transportation, logistics, or supply chain.
USA: $5,364\.26/mo \- $11,801\.37/mo, CO: $5,364\.26/mo \- $11,309\.65/mo, CA: $5,662\.27/mo \- $9,476\.86/mo, NJ: $5,662\.27/mo \- $9,059\.63/mo, OH \& VT: $5,662\.27/mo \- $9,342\.75/mo, MN: $5,662\.27/mo \- $10,817\.92/mo, IL \& NV: $5,662\.27/mo \- $11,309\.65/mo, MD, NY \& WA: $5,662\.27/mo \- $11,801\.37/mo, MA: $5,960\.29/mo \- $11,801\.37/mo, RI: $6,556\.32/mo \- $10,817\.92/mo, CT: $6,556\.32/mo \- $11,309\.65/mo, DC \& HI: $6,854\.33/mo \- $11,309\.65/mo, NYC: $6,854\.33/mo \- $11,801\.37/mo
Data Scientist II (GCP \& AI Engineer)
Overview
Elevate your career as a Data Scientist II and become a cornerstone of our innovation. We are seeking a highly skilled and experienced Data Scientist who deeply understands that impact comes not just from building cutting\-edge models, but from owning their entire lifecycle from conception to production\-ready deployment. If you are passionate about architecting, developing, and operationalizing robust, end\-to\-end machine learning and analytics solutions, integrating seamlessly with diverse APIs, and championing the adoption of Generative AI, then this role is for you. Leveraging the power of the Google Cloud Platform (GCP) ecosystem, you will lead critical projects, mentor junior team members, and drive measurable business value in collaboration with product, engineering, and business stakeholders.
Responsibilities
- End\-to\-End ML Ownership: Lead the complete lifecycle of analytics and ML projects, from problem definition and data discovery to feature engineering, model development, validation, deployment, and continuous monitoring.
- Vertex AI Development: Architect, develop, and productionize advanced predictive and prescriptive models (classification, regression, clustering, time series, optimization), effectively serving them via Vertex AI Pipelines and Endpoints.
- Generative AI Implementation: Design, develop, and deploy practical Generative AI solutions (e.g., Retrieval\-Augmented Generation/RAG architectures, prompt design) using Vertex AI Gen AI capabilities.
- Robust Feature Engineering: Design and implement robust feature stores and transformation logic to feed training and real\-time inference workflows, ensuring model input consistency and drift mitigation.
- API Design \& Deployment: Expertly design, build, and integrate RESTful APIs (e.g., FastAPI, Flask) for seamless model serving and microservice\-based ML system integration.
- MLOps \& CI/CD Practices: Implement advanced MLOps and CI/CD strategies for models and serving pipelines, encompassing automated testing, deployment pipelines, model registry/tracking, and comprehensive drift and performance monitoring.
- BigQuery Query Optimization: Optimize data models, analytical views, and SQL queries to support efficient, low\-latency machine learning training and inference workloads.
- Reporting \& Visualizations: Develop compelling, interactive dashboards using Looker/Looker Studio to translate complex insights into clear business actions and quantifiable KPIs.
- Mentorship \& Collaboration: Mentor and technically guide junior data scientists and analysts, actively contributing to team\-wide best practices, documentation, and troubleshooting production incidents.
- Domain Alignment: Cultivate deep domain expertise in FedEx data and business operations to align ML efforts with immediate team goals.
Required Technical Skills
- Core Languages: Expert proficiency in Python and SQL.
- ML Frameworks: Extensive experience with leading ML frameworks (scikit\-learn, XGBoost, TensorFlow, or PyTorch) and a proven track record of deploying them to production environments.
- Generative AI Platforms: Hands\-on experience developing solutions utilizing GenAI models (e.g., working with LLMs, prompt engineering, and utilizing Vertex AI Model Garden).
- GCP Ecosystem: Deep hands\-on experience with Google Cloud Platform (GCP) for end\-to\-end ML, specifically BigQuery, Vertex AI, Vertex AI Feature Store, and Cloud Storage (GCS).
- API Architecture: Proficiency in designing, building, and securing APIs (e.g., FastAPI, Flask) to expose ML microservices.
- Performance Tuning: Expertise in performance tuning, SQL query optimization, and the design of efficient, scalable models for ML data preparation.
Preferred Qualifications
- GCP Certifications: Professional Google Cloud Machine Learning Engineer certification.
- Observability \& Data Quality: Experience with monitoring, observability, and automated data\-quality tools (e.g., Great Expectations, GCP Cloud Monitoring, Cloud Logging for ML model performance).
- Logistics Domain: Domain expertise in logistics, transportation, supply chain, or related industries.
Minimum Education:
Master’s degree (or equivalent) in Computer Science, Operations Research, Statistics, Applied Mathematics, or a related quantitative field.
Minimum Experience:
At least two (2\) years of professional experience applying data science (e.g., machine learning, artificial intelligence, statistical analysis), operations research (e.g., optimization, algorithms, mathematical modeling), and data analytics to reduce costs, enhance profitability, and improve customer experience. An advanced degree in a related field may be considered in lieu of some experience.
USA: $6,168\.90/mo \- $13,571\.58/mo, CO: $6,168\.90/mo \- $13,006\.09/mo, CA: $6,511\.62/mo \- $10,898\.39/mo, NJ: $6,511\.62/mo \- $10,418\.59/mo, OH \& VT: $6,511\.62/mo \- $10,744\.16/mo, MN: $6,511\.62/mo \- $12,440\.61/mo, IL \& NV: $6,511\.62/mo \- $13,006\.09/mo, MD, NY \& WA: $6,511\.62/mo \- $13,571\.58/mo, MA: $6,854\.33/mo \- $13,571\.58/mo, RI: $7,539\.76/mo \- $12,440\.61/mo, CT: $7,539\.76/mo \- $13,006\.09/mo, DC \& HI: $7,882\.48/mo \- $13,006\.09/mo, NYC: $7,882\.48/mo \- $13,571\.58/mo
This position is eligible for remote work and may be located anywhere within the United States excluding AK, HI and U.S. territories, however if you live within the 50 miles radius of a campus you will be required to work at a FedEx campus location several times per week.
Preferred Qualifications:
Pay Transparency:
Pay:
Additional Details:
FedEx Dataworks is an Equal Opportunity Employer including, Vets/Disability.
- Know Your Rights
- Pay Transparency
Dataworks does not discriminate against qualified individuals with disabilities in regard to job application procedures, hiring, and other terms and conditions of employment. Further, Dataworks is prepared to make reasonable accommodations for the known physical or mental limitations of an otherwise qualified applicant or employee to enable the applicant or employee to be considered for the desired position, to perform the essential functions of the position in question, or to enjoy equal benefits and privileges of employment as are enjoyed by other similarly situated employees without disabilities, unless the accommodation will impose an undue hardship. If a reasonable accommodation is needed, please contact [email protected].
Salary Context
This $78K-$162K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At FedEx, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($120K) sits 38% below the category median. Disclosed range: $78K to $162K.
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 Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
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).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
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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