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Data Scientist

Job in Cincinnati, Hamilton County, Ohio, 45208, USA
Listing for: Agility Partners
Full Time position
Listed on 2026-10-15
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 120000 - 190000 USD Yearly USD 120000.00 190000.00 YEAR
Job Description & How to Apply Below

Our client is seeking experienced Data Scientists to join a Search, Recommendations, and Personalization team responsible for building intelligent customer experiences at scale.

This team develops machine learning solutions that improve how customers discover products, promotions, coupons, deals, and content across digital experiences. The role focuses on recommendation systems, personalization, search relevance, ranking, and customer engagement challenges using large-scale behavioral and transaction data.

Responsibilities
  • Design, develop, and optimize machine learning models supporting personalization, recommendations, and search experiences.
  • Build recommendation systems for products, promotions, coupons, deals, and content recommendations.
  • Develop features from large-scale customer, transaction, and behavioral datasets.
  • Design and execute experiments to evaluate model performance and business impact.
  • Partner closely with Product Owners to translate business needs into scalable machine learning solutions.
  • Perform statistical analysis, feature engineering, and model evaluation to drive customer engagement and personalization improvements.
Required Qualifications
  • 8+ years of professional experience in Data Science, Machine Learning, Applied Science, or a related field.
  • Proven experience building recommendation systems, personalization platforms, search relevance models, ranking systems, or similar machine learning solutions.
  • Strong expertise in Python, Spark, SQL, machine learning, and statistical modeling.
  • Experience working with Azure, Databricks, and large-scale cloud data platforms.
  • Experience designing experiments and measuring model performance through A/B testing and other evaluation techniques.
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