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About This Role
Intern/Co-Op sessions are considered temporary employment. No full-time employment commitments are made. However, depending on our business needs, some interns may be considered for a full-time position following the conclusion of the session conditioned upon the intern graduating from their academic program and being available to commence full-time employment at a specified time.
JOB SUMMARY
The Decision Support organization turns Inventory Solutions' most complex questions into fast, confident decisions, combining business knowledge, mathematics, AI and machine learning, and software engineering to produce the models, applications, and platforms behind those decisions. Within Decision Support, the Strategic Solutions & Enablement (SSE) team sets the shared architecture, tooling, and methodology standards used across the organization and builds the internal-facing decision surfaces teams rely on across areas like transaction intelligence, logistics decisioning, and operational optimization.
As a Data Scientist Co-op on the SSE team, you will work alongside experienced data scientists and engineers on real projects that support business decision-making. This is a multi-term co-op designed to give a student progressively deeper hands-on experience across several terms. You will learn how production data science is done end to end - from framing a business question, to exploring data, to building and validating models, to communicating results - under the guidance and mentorship of the team. You are not expected to own products independently; you are expected to be curious, learn quickly, and contribute meaningfully to the team's work.
PRIMARY DUTIES / KEY RESPONSIBILITIES
Learning & Applied Analytics
- Assist data scientists in exploring, cleaning, and preparing data from a variety of internal sources for analysis and modeling.
- Write Python and SQL to extract, transform, and analyze data, building your proficiency with the team's tools and standards.
- Help build, test, and tune machine learning models under guidance, learning classical methods (e.g., regression, gradient boosting, clustering) and applied generative AI approaches.
- Support the creation of prototypes, demos, and visualizations that show how data and AI can be used to solve business problems.
Collaboration & Communication
- Participate in team standups, design discussions, and code/peer reviews, gradually taking on more responsibility across terms.
- Document your work clearly so it can be understood, reproduced, and built on by the team.
- Present findings and progress to the team and, over time, to business partners, adjusting depth for the audience
Growth Across Terms
- Return across multiple co-op terms, alternating semesters, building continuity and increasing scope and independence with each term.
- Apply feedback and coursework to progressively more substantial contributions to SSE projects.
MINIMUM QUALIFICATIONS
- Currently enrolled in a Bachelor's degree program in Computer Science, Data Science, Statistics, Industrial Engineering, or a related quantitative field.
- Able to commit to multiple co-op terms, alternating semesters, per a standard university co-op model.
- Foundational Python programming skills (e.g., coursework or projects using Pandas, NumPy, or scikit-learn).
- Working knowledge of SQL and an interest in learning cloud data platforms such as Snowflake and AWS.
- Exposure to statistics and/or machine learning concepts through coursework or projects.
- Strong analytical thinking and a demonstrated ability to learn quickly and work as part of a team.
- Clear written and verbal communication skills
PREFERRED QUALIFICATIONS
- Coursework or personal/academic projects involving machine learning, generative AI, or data visualization (e.g., Tableau, Streamlit).
- Familiarity with version control (Git) and collaborative development practices.
- Interest in the automotive industry or in large-scale marketplace data.
CORE VALUES & BEHAVIORS
- Acts with Integrity: Adheres to ethical standards, organizational policies, and personal values.
- Builds Partnerships: Fosters trusting relationships with teammates and stakeholders.
- Collaborates with Intent: Communicates effectively and welcomes feedback to drive better outcomes.
- Develops Trust: Practices transparency and embraces diverse viewpoints.
- Drives Innovation: Explores new ways to solve problems and is willing to experiment and learn.
WHY JOIN OUR TEAM
- Get real, hands-on data science experience on products that operating teams use daily to make decisions.
- Work with modern cloud-native platforms (Snowflake, AWS, Bedrock), applied AI tooling, and rich automotive data at scale.
- Learn directly from experienced data scientists and engineers who mentor through doing, not just formal training.
- Build continuity and depth across multiple terms, with a genuine path toward future full-time opportunities.
Travel: 0-10%
Hybrid - ability to work in-office 1-3 days per week.
USD 22.02 - 33.08 per hour
Compensation:
Hourly pay rate is in the range of $22.02 - $33.08/hour. The hourly base rate may vary within the anticipated range based on factors such as the ultimate location of the position and the selected candidate's knowledge, skills, and abilities. Position may be eligible for additional compensation that may include commission (annual, monthly, etc.) and/or an incentive program.
Benefits:
Benefits of working at Cox may include health care insurance (medical, dental, vision), retirement planning (401(k)), and paid days off (sick leave, parental leave, flexible vacation/wellness days, and/or PTO). For more details on what benefits you may be offered, visit our benefits page.
Applicants must currently be authorized to work in the United States for any employer without current or future sponsorship.
EOE, including disability/vets
Salary Context
This $45K-$68K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Cox Automotive, 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 $190,000 based on 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($57K) sits 70% below the category median. Disclosed range: $45K to $68K.
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.
Cox Automotive AI Hiring
Cox Automotive has 1 open AI role right now. They're hiring across Data Scientist. Based in Atlanta, GA, US. Compensation range: $68K - $68K.
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 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 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).
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 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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