- Location
- Grand Rapids, MI, United States, United States of America · Wauwatosa, WI, United States · Henrietta, NY, United States · Atlanta, GA, United States · Plano, TX, United States · Salt Lake City, UT, United States
- Type
- Full-time
- Department
- Operations
- Source
- Workday
Description
You will partner closely with business stakeholders across functions to understand their challenges, design analytical solutions, and communicate findings in ways that drive real outcomes. This is a high-impact, hands-on role suited for someone who is as comfortable building and deploying models as they are presenting insights to non-technical audiences.
We offer:
Key Responsibilities
Develop, validate, and deploy machine learning and statistical models to support operational decision-making
Collaborate with business partners to frame problems, identify data needs, and translate analytical outputs into actionable recommendations
Work within a modern cloud-based data platform environment (including Databricks) to access, transform, and analyze data at scale
Contribute to the development of repeatable analytical frameworks, pipelines, and best practices as the team matures
Ensure data quality and discoverability across the analytics environment
Communicate complex findings clearly to both technical peers and non-technical stakeholders
Help build a data-driven culture by demonstrating the value of analytics through practical, high-visibility work
Tasks and Qualifications:
Skills & Capabilities
Proficiency in Python and/or R, with strong SQL skills for data access and manipulation
Demonstrated experience building and deploying ML models (regression, classification, clustering, forecasting)
Experience with cloud-based data platforms; familiarity with Databricks or similar tools (e.g., Snowflake, Azure ML) is a plus
Comfort working with large-scale distributed data in a cloud environment (Azure, AWS, or GCP)
Strong communication skills with the ability to translate analytical work into business impact
A self-starter mindset that is comfortable with some ambiguity and energized by the chance to build something meaningful
A collaborative, intellectually curious approach with a bias toward practical application over theoretical elegance