Senior Data Scientist & Machine Learning Engineer
Key Skills for This Role
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Responsibilities
- 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
- 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.
Preferred Qualifications
- 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.
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