Job Description

SENIOR DATA SCIENTIST, PREDICTIVE ANALYTICS

Advanced Analytics & Machine Learning

Chicago, IL | Hybrid, 3 Days Onsite

Up to $170,000 Base + Bonus

THE COMPANY

Join a globally recognized organization at an exciting point in the evolution of its data and analytics capabilities.

The business is making significant investments in how it uses data, moving beyond traditional reporting toward more sophisticated predictive analytics and data-driven decision-making across the organization.

You will join a lean, highly collaborative analytics team where you can have genuine influence. Rather than becoming one Data Scientist within a large, rigid function, you'll have the opportunity to own meaningful problems, work closely with senior stakeholders, and help shape how data science is applied across the business.

THE OPPORTUNITY

This is a role for a Data Scientist who wants to do more than optimize an existing model or own one small piece of a larger pipeline.

You'll take complex, often ambiguous business problems and turn them into analytical solutions from the ground up.

That means understanding the underlying business challenge, finding and preparing the right data, determining the appropriate modeling approach, building and evaluating predictive models, and ultimately translating your findings into recommendations the business can act on.

Your initial work will span areas including forecasting, market analysis, profitability, and predictive decision-making, with significant scope to identify and develop entirely new data science use cases as the capability grows.

WHAT YOU'LL DO

• Own high-impact data science projects from initial business question through modeling, analysis, and delivery
• Work directly with stakeholders to understand complex problems and translate them into practical analytical solutions
• Build predictive and statistical models using Python and SQL
• Apply techniques including regression, classification, forecasting, clustering, and feature engineering
• Explore, clean, validate, and transform complex datasets rather than relying on perfectly prepared data
• Determine the right analytical or modeling approach for each problem rather than simply applying predefined methodologies
• Evaluate model performance and translate technical findings into clear b

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