Amazon Research Jobs in Ohio (NOW HIRING)

If you like turning real-world problems into models that actually move the needle, you’ll fit right in. Our Workforce Planning (WFP) Data Science team builds AI/ML solutions that help forecast demand, optimize capacity, and sharpen scheduling at the scale of Chase call centers, back office operations, and ~5,200 branches. The work is complex, high-impact, and rarely “plug-and-play”—you’ll frame the problem, choose the right math, and deliver solutions built for action. If real-time decisioning sounds like your kind of challenge, this is where you belong.

As a Data Scientist Associate Senior within Consumer and Community Banking in Workforce Planning (WFP), you will solve complex, data-intensive, and often unstructured business problems that materially impact operations. You will independently frame ambiguous questions, translate them into mathematical formulations, and select modeling approaches grounded in strong theoretical fit—not just benchmarking off-the-shelf algorithms. You will design, develop, and productionize rigorous AI/ML solutions that advance next-generation capabilities, including moving toward real-time inference and decision-making across Workforce Planning.

• Design and development of Machine Learning, Artificial Intelligence and Statistical models.
• Support AI/ML projects individually or as part of a project team.
• Collaborate with stakeholders to understand the business requirements and clearly define the objectives of any solution.
• Select and apply the appropriate modeling approach based on principled understanding of the problem structure and underlying assumptions, while staying up to date on the latest AI/ML research.
• Ensure the robustness of any data science solution.
• Develop and communicate recommendations and data science solutions in an easy-to-understand way leveraging data to tell a story.
• Work effectively and gain credibility and respect of others including peers, clients and other stakeholders.

• Master’s Degree with 3+ years or Doctorate (PhD) with 1+ years of experience operating as a data science professional in a quantitative field (e.g., Statistics, Analytics, Data Science, Engineering, Operations Research, Economics, Mathematics, Machine Learning, Artificial Intelligence).
• Experience supporting AI/ML projects with multiple team members.
• Hands-on experience developing statistical models, machine learning models, and/or artificial intelligence models.
• Foundational understanding of the math and theory behind AI/ML algorithms, with the ability to reason about model assumptions, behavior, and limitations beyond empirical validation.
• Proficient in data science programming languages like Python, R or Scala.
• Experience with big-data technologies such as Hadoop, Spark, SparkML, etc., and familiarity with basic data table operations (SQL, Hive, etc.).
• Demonstrated relationship-building skills, with a superior ability to make things happen through positive influence.

• Advanced expertise with Time Series and Operations Research techniques.
• Natural Language Processing (NLP) / Natural Language Generation (NLG), Neural Nets, or other ML/AI skills.
• Prior experience with public cloud technologies such as Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP).
• Previous experience leading highly complex cross-functional technical projects with multiple stakeholders.

This role is not eligible for visa sponsorship. This role is 5 days a week full time in office.

Chase is a leading financial services firm dedicated to helping households and small businesses achieve their financial goals through a broad range of products. We value diversity and inclusion and are committed to equal opportunity employment.

We offer a competitive total rewards package including base salary determined by role, experience, skill set and location. Benefits include health care coverage, retirement savings, and additional programs eligible to employees. Additional details about total compensation and benefits will be provided during the hiring process.