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About This Role
Senior Data Scientist \& Machine Learning Engineer
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Date: Aug 10, 2026
Location: New York, NY, US
Summary
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We are seeking a Senior Data Scientist / Machine Learning Engineer to support Chobani’s Data \& Analytics organization with a strong focus on measurement, forecasting, planning, optimization, machine learning, and AI\-enabled decision support.
This role will partner closely with Retail Execution, Commercial, Finance, Supply Chain, Analytics Engineering, Data Engineering and other cross\-functional teams to translate business questions into scalable analytical and machine learning solutions. The ideal candidate combines strong data science and machine learning expertise with practical software engineering skills, business partnership, and the ability to build reusable tools that support decision\-making across the organization.
The initial focus of this role may include retail execution use cases, such as measuring the impact of store\-level interventions, forecasting sales and order trends, out of stock modeling, identifying execution risks, and developing models that help prioritize field activity. Over time, this role may support broader One Chobani initiatives across commercial analytics, planning, operations, AI enablement and advanced analytics.
Responsibilities
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Data Science, Measurement \& Forecasting
- Design and develop data science models to support retail execution measurement, forecasting, planning, optimization and decision support across business functions.
- Build reusable approaches to measure the incremental impact of store\-level activities such as coolers, displays, sampling, field visits, promotions, out\-of\-stock interventions and other commercial programs.
- Develop forecasting models for sales, orders, demand pacing, out\-of\-stock risk, store\-level performance trends and other business planning needs.
- Apply statistical modeling, machine learning, causal inference, optimization, supervised learning, time\-series forecasting, and other advanced analytical techniques to answer complex business questions.
- Create clear diagnostics, assumptions, and confidence measures so model outputs can be understood and trusted by business stakeholders.
Machine Learning Engineering \& AI Enablement
- Build scalable, reusable Python\-based tools, libraries, models, and analytical services that can support multiple business use cases.
- Partner with Data Engineering and Analytics Engineering teams to integrate models with Snowflake, dbt, POS data, retail execution data, master data, and external data sources.
- Support AI\-enabled use cases including image analysis, OCR, NLP / LLM workflows, anomaly detection, and recommendation models.
- Help convert prototypes and proof\-of\-concepts into production\-grade solutions with appropriate documentation, testing, monitoring, and deployment standards.
- Identify opportunities to automate manual analysis and create repeatable decision\-support capabilities.
Business Partnership \& Communication
- Partner with Retail Execution and cross\-functional business teams to understand priorities, define analytical requirements, and translate business needs into technical solutions.
- Communicate complex modeling approaches and trade\-offs clearly to both technical and non\-technical audiences.
- Synthesize model outputs into actionable recommendations, business readouts, dashboards, alerts, or decision\-support tools.
- Proactively identify high\-value data science opportunities that can improve execution, resource allocation, forecasting accuracy, and commercial performance.
Data Strategy \& Enablement
- Contribute to the development of reusable data science frameworks, model standards, and best practices across the Data \& Analytics organization.
- Collaborate with data teams to improve data quality, store and product linkage, common event definitions, and analytical foundations required for scalable modeling.
- Support the broader roadmap for AI, machine learning, and advanced analytics across Chobani.
Requirements
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The requirements of this position include:
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics or relevant quantitative field.
- 5\+ years of experience in data science, machine learning, applied statistics, analytics engineering, or related roles; or Master’s degree with 3\+ years of relevant experience.
- Strong hands\-on experience with Python and SQL.
- Experience building machine learning, forecasting, statistical modeling, or causal inference solutions in a business environment.
- Strong understanding of time\-series forecasting, regression, classification, model evaluation, and experimental or quasi\-experimental measurement methods.
- Experience with forecasting tools and methods such as Prophet, ARIMA, machine learning forecasting models or similar approaches is preferred.
- Experience working with modern data platforms such as Snowflake, dbt, Git, cloud environments or similar tools.
- Experience with production\-grade model development, including testing, monitoring, drift detection, retraining, and model accuracy evaluation.
- Ability to build practical, reusable analytical tools beyond one\-off notebooks.
- Demonstrated ability to partner with business stakeholders, operate independently, and deliver results in ambiguous environments.
- Strong communication skills with the ability to explain technical concepts, assumptions, and recommendations to non\-technical audiences.
- Experience in CPG, retail, retail execution, commercial analytics, supply chain, or revenue growth management is strongly preferred.
- Experience with AI / ML tools, computer vision, OCR, NLP, LLMs, MLOps, or production model deployment is preferred.
About Us
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Chobani is a food maker with a mission of making high\-quality and nutritious food accessible to more people, while elevating our communities and making the world a healthier place. In short: making good food for all. In support of this mission, Chobani is a purpose\-driven, people\-first, food\-and\-wellness\-focused company, and has been since its founding in 2005 by Hamdi Ulukaya, an immigrant to the U.S. The Company manufactures yogurt, oat milk, and creamers – Chobani yogurt is America's No.1 yogurt brand, made with natural ingredients without artificial preservatives. Following the 2023 acquisition of La Colombe, a leading coffee roaster with a shared commitment to quality, craftmanship and impact, the Company began selling cold\-pressed espresso and lattes on tap at cafés nationwide, as well as Ready to Drink (RTD) coffee beverages at retail. In 2025, Chobani acquired Daily Harvest, a modern brand offering consumers nutritious, delicious and convenient ready\-to\-make meals.
Chobani uses food as a force for good in the world – putting humanity first in everything it does. The company's philanthropic efforts prioritize giving back to its communities and beyond. Chobani manufactures its products in New York, Idaho, Michigan and Australia, and its products are available throughout North America and distributed in Australia and other select markets.
For more information, please visit www.chobani.com or follow us on Facebook, Twitter, Instagram and LinkedIn.
*Chobani is an equal opportunity employer. Chobani will not discriminate against any applicant for employment on any basis including, but not limited to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, military and/or veteran status, marital status, predisposing genetic characteristics and genetic information, or any other classification protected by federal, state, and local laws.*
The salary range for this full\-time position is $105,500\.00 \- $196,500\.00, \+ bonus \+ equity \+ benefits. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Chobani provides a comprehensive benefits package, including medical, dental, vision coverage, disability insurance, health savings account, flexible spending accounts, and tuition reimbursement. To help save for the future, all employees are eligible for a 401k match of 100% on up to 5% of eligible pay. To support growing families, we provide fertility and childcare assistance, and 12 weeks of parental leave at full pay after six months of continuous employment. In addition, we provide wellness resources which include an employee assistance program, fitness discounts, a wellness reimbursement, on\-site gym access (certain locations) and a monthly wellness newsletter to connect you with resources and timely information. We offer various types of paid time of including: 120 hours of paid time off, 11 holidays, and paid volunteer time off.
Salary Context
This $105K-$196K range is below the median 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 Chobani, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($151K) sits 22% below the category median. Disclosed range: $105K to $196K.
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.
Chobani AI Hiring
Chobani has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $196K - $196K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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