Senior Data Scientist & Machine Learning Engineer

Chobani•New York, NY

1d•$105,500 - $196,500

About The Position

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.

Requirements

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 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.

Nice To Haves

Experience with forecasting tools and methods such as Prophet, ARIMA, machine learning forecasting models or similar approaches is preferred.
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.

Responsibilities

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.
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.
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.
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.

Benefits

medical, dental, vision coverage
disability insurance
health savings account
flexible spending accounts
tuition reimbursement
401k match of 100% on up to 5% of eligible pay
fertility and childcare assistance
12 weeks of parental leave at full pay after six months of continuous employment
wellness resources
employee assistance program
fitness discounts
a wellness reimbursement
on-site gym access (certain locations)
a monthly wellness newsletter
120 hours of paid time off
11 holidays
paid volunteer time off