Expert Data Scientist

Nestle Operational Services Worldwide SA•Arlington, VA

3d•$162,386 - $225,690

About The Position

Join Nestlé IT & Digital as an Expert Data Scientist supporting Nestlé USA. The role combines business insights with AI/ML and data science to drive analytics initiatives across the organization. The Expert Data Scientist collaborates with business functions to identify opportunities and develop use cases, then designs, enhances, and implements data science and AI/ML solutions. This includes building verification and workflow scaffolding so product teams can ship reliably. Success is measured through durable impact, adoption, and measurable business value.

Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Data Science, or a related field.
7+ years of professional experience, including 5+ years building and deploying production AI/ML solutions adopted by end users and scaling solutions in enterprise environments using MLOps, cloud data platforms, and emerging AI/ML technologies.
3+ years of experience developing with Python/PySpark to automate tasks, manipulate data, and build production-level data science solutions in an MLOps environment.
3+ years of experience managing stakeholders with minimal oversight, maintaining relationships, presenting to senior executives, and communicating high-level technical concepts in clear, colloquial, action-oriented language.
2+ years of experience mentoring or coaching junior peers and teammates.

Nice To Haves

Master's or PhD in Computer Science, Engineering, Mathematics, Statistics, Data Science, or a related discipline.
Experience in consumer products, CPG, or Commercial environments.

Responsibilities

Lead end-to-end development and deployment of machine learning models, from experimentation and evaluation through production deployment, adoption, and ongoing optimization, aligned with business priorities.
Partner with business stakeholders to translate complex or ambiguous challenges into technical requirements, defining data needs, success criteria, tradeoffs, risks, and actionable solutions that deliver measurable business value.
Develop and deploy solutions in enterprise cloud environments, including automating jobs, provisioning resources, deploying packages, and navigating enterprise security requirements.
Design evaluation, monitoring, and validation frameworks for production AI/ML solutions, identifying model, data-quality, and pipeline issues to maintain reliability and effectiveness at scale.
Partner with global and regional IT & Digital teams to scale analytics and ML capabilities, navigating enterprise platforms, roadmaps, and processes to enable adoption.
Document technical decisions and learnings, coach teammates and business users, and apply security, resilience, and responsible AI/ML principles throughout solution development.