Operational Data Scientist

About the role

We are growing data and analytics capability at an exciting time, offering an opportunity to help shape a modern analytics practice within an established enterprise. You will work at the intersection of advanced analytics, machine learning, and business problem-solving, turning complex datasets into actionable insights that directly influence strategy and performance. This high-impact, hands-on role is suited for someone who is as comfortable building and deploying models as they are presenting insights to non-technical audiences.

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

Requirements

• 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