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About This Role
fairlife, LLC is a Chicago-based nutrition company that creates great-tasting, nutrition-rich and dairy products to nourish consumers.
With over $3B in annual retail sales, fairlife's portfolio of delicious, lactose-free, real dairy products includes: fairlife® ultra-filtered milk; Core Power® High Protein Shakes, a sports nutrition drink to support post-workout recovery; fairlife® nutrition plan™, a nutrition shake to support the journey to better health.
A wholly owned subsidiary of The Coca-Cola company, fairlife, LLC has been recognized by both Fast Company and Nielsen for its industry leading innovation.
To learn more about fairlife and its complete line of products, please visit fairlife.com.
job purpose: The Data Scientist will play a key role in advancing fairlife's vision of establishing a world-class Decision Intelligence ecosystem. This role entails supporting the design, development, and delivery of AI and ML models including production optimization, quality control and anomaly detection, predictive maintenance, supply chain forecasting, and real-time operational analytics. The role will drive innovation and excellence in data science capability, empowering fairlife to leverage data-driven insights for enhanced decision-making and operational efficiency. Increasingly, this work also includes applying large language models (LLMs) and AI agents to automate workflows and augment traditional models where they add value.
responsibilities:
- Collaborate with the Senior Data Scientist to design, develop, and deploy AI and ML models supporting analytics and business objectives
- Engineer and maintain scalable data pipelines for AI/ML workflows
- Enable AI adoption by developing reusable components, APIs, and automation scripts
- Support model lifecycle management, including monitoring, retraining, and performance optimization
- Implement MLOps best practices for version control, CI/CD, and automated deployment
- Assist in the development of advanced analytics dashboards and tools to communicate model results
- Apply prompt engineering, retrieval-augmented generation (RAG), and fine-tuning to build effective large language model (LLM) solutions
- Build and integrate LLM-powered agents to automate multi-step tasks and workflows that complement traditional ML models
- Ensure responsible AI practices, including fairness, transparency, and compliance
- Gather, clean, and analyze large and complex datasets to support AI initiatives
- Conduct experiments and statistical analyses to answer business questions and validate AI models
- Stay current with emerging AI technologies and recommend innovative solutions
skills/qualifications required:
- 2-4+ years of relevant experience in data science, AI engineering, or related fields
- Bachelor's or Master's in Data Science, Computer Science, Statistics, Engineering, or a related STEM field
- Experience in data cleaning, exploration, and analysis
- Hands-on experience with machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
- Proficiency in Python and SQL for data manipulation, analysis, and model development
- Familiarity with cloud platforms (Azure/Fabric, AWS, or GCP) for scalable AI/ML deployment
- Experience with MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines)
- Experience applying large language models (LLMs), including prompt engineering and fine-tuning, and responsible AI practices
- Ability to visualize data and model results using tools like Power BI, Tableau, Matplotlib, or Seaborn
- Strong communication skills for collaborating with business and technical teams
- Exposure to building AI agents or agentic workflows (e.g., LangChain, LangGraph) a plus
- Experience in manufacturing and supply chain (preferred)
how fairlife nourishes you:
At fairlife, we believe in better — and that includes how we support our people. We offer a comprehensive suite of benefits and wellbeing resources designed to support you physically, emotionally, socially, and financially, both in and out of work.
- Comprehensive medical, dental, and vision coverage, effective day one!
- Supplemental health plans (hospital indemnity, accident, and critical illness insurance)
- Paid Time Off to recharge and support work-life balance
- Paid parental leave & adoption assistance (up to $10,000)
- Parental support & family care benefits, including childcare resources and lactation support
- 401(k) to support retirement planning with up to 9% in employer match
- Wellness reimbursement (up to $500 for qualified wellbeing expenses)
- Employee Assistance Program (EAP) for emotional wellbeing and work-life support
- Company-paid life insurance and short-term disability
- Employer HSA funding (for HDHP participants)
- Tuition reimbursement (up to $10,000) and student loan repayment ($200/month)
- Learning & development programs to unlock your full potential
- Rewards & recognition, matching gifts, free product, and Business Resource Groups
fairlife's nour!sh program is designed to meet you where you are — supporting your individual wellbeing journey while enabling you to do your best work every day.
location: Chicago, IL
reports to: Senior Data Scientist, Decision Intelligence
travel requirements: <10%
exempt/nonexempt: exempt
fairlife, LLC is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. All qualified applicants and employees will be given equal opportunity. Selection decisions are based on job-related factors.
In addition to its nondiscrimination commitment, the Company will also provide reasonable accommodation of qualified individuals with known disabilities unless doing so would impose an undue hardship on the Company. If you have a disability and would like to request accommodation in order to apply for a position with us, please email [email protected].
For Recruitment Agencies
At fairlife, we manage the majority of our hiring internally through our dedicated Talent Acquisition team, which is actively engaged in direct candidate sourcing. Most of our roles are filled through applications submitted via our careers site or through direct outreach by our team.
As our recruitment is primarily handled in-house, we work only occasionally with external agencies, and only those on our existing, pre-approved vendor list. At this time, we are not reviewing or expanding that list.
Unsolicited resumes or submissions from external agencies not authorized by our Talent Acquisition team will be considered direct candidate applications. As such, fairlife will not assume responsibility for any placement fees associated with these submissions.
Salary Context
This $95K-$110K range is in the lower quartile for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Fairlife, LLC, 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 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($102K) sits 46% below the category median. Disclosed range: $95K to $110K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Fairlife, LLC AI Hiring
Fairlife, LLC has 1 open AI role right now. They're hiring across Data Scientist. Based in Chicago, IL, US. Compensation range: $110K - $110K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% below the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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