Sr. Data Scientist (Forecasting)

Nestle Operational Services Worldwide SA•Arlington, VA

5d•$110,000 - $160,000

About The Position

As the digital arm for the world's largest food and beverage company, Nestlé IT & Digital Americas harnesses the power of data, analytics, and innovative technology to deliver transformative solutions and business resiliency for Nestlé businesses and iconic brands from Purina® to Nescafé®. We’re innovators, strategic collaborators, value multipliers, and digital business leaders committed to delivering results that create meaningful experiences and drive value, enabling Nestlé to win in the marketplace. By joining, you become part of a 150-year legacy and a global team of 270,000+. We invest in our people—their growth, development, and sense of belonging. This shared unity and purpose is what motivates individuals to join, stay, and advance their careers within Nestlé. This position is not eligible for Visa Sponsorship. Position Summary Join Nestlé IT & Digital as a Sr. Data Scientist supporting Nestlé USA Digital, where you'll partner with the Supply Chain Planning team and the Analytics team to build the forecasting solutions that power how Nestlé USA plans and operates at scale. You'll design and deploy production-grade forecasting models, embed AI into how forecasts get delivered, and serve as a key liaison between the technical and functional sides of Enterprise Forecasting. You'll bring a strong AI mindset, curiosity about emerging technologies, and comfort operating in ambiguity as the forecasting landscape evolves.

Requirements

Bachelor's degree in statistics, mathematics, economics, business, or a related field
5+ years of experience in data science, forecasting, or advanced analytics, with hands-on experience in SQL, Python, or a similar language
5+ years of experience extracting, transforming, and analyzing large datasets, and creating data visualizations to communicate insights
3+ years of experience deploying and maintaining machine learning models in production, including regression, classification, time series modeling, and feature engineering

Nice To Haves

Master's degree in statistics, mathematics, economics, business, or a related discipline is preferred
Experience with ML Ops practices, cloud and AI/ML platforms (Databricks, Snowflake, Microsoft Azure), GenAI solutions, and Microsoft Power BI is preferred
Experience supporting enterprise-scale transformation or Supply Chain planning initiatives in an Agile environment is preferred

Responsibilities

Design, develop, test, and deploy statistical and machine learning forecasting models in production on cloud-based analytics platforms, with a focus on demand pattern recognition, algorithm selection, outlier correction, and parameter optimization to drive forecast accuracy and bias reduction
Build and maintain structured, programmatic forecasting processes that deliver at scale, leveraging ML Ops practices to automate weekly forecast generation and reduce manual effort
Embed Agentic AI and GenAI capabilities into forecasting workflows to identify accuracy improvement opportunities, automate root cause analysis, and support insights generation and decision-making
Continuously explore and apply emerging AI/ML techniques such as deep learning and probabilistic forecasting to improve forecast accuracy and scalability across a complex portfolio of SKUs, clusters, and data sources
Serve as a liaison between technical and functional teams, partnering with Supply Chain Planning leads and cross-functional stakeholders across Marketing, Finance, and Sales to align business needs with system and process solutions
Advance Enterprise Forecasting as part of the One Planning initiative and support Customer Order Fulfilment improvements, enabling standardized, scalable, and cross-functional forecasting solutions across the organization
Deliver training and forecast office hours to Supply Chain Planning users to build analytics literacy, promote adoption, and increase enterprise value
Create and enhance Power BI reports for forecast KPI monitoring and data validation, and present findings through data visualization and PowerPoint tailored to the audience