Location: Chicago, IL, United States

Join our Global Services Insights & Analytics Team and be at the forefront of transforming data into actionable insights. This role offers a unique opportunity to collaborate with senior executives and make a significant impact on operational performance and efficiency.

As a Data Scientist in the Global Services Insights & Analytics Team, you will lead data-driven initiatives that enhance planning, efficiency, service, and controls within Commercial Banking. You will work closely with senior executives, leveraging advanced data techniques to drive continuous improvements and deliver impactful insights.

Job Responsibilities:

• Lead data analytics and insight generation initiatives using hands-on data science techniques.
• Develop experimental frameworks to facilitate data collection.
• Define comprehensive analytics approaches leveraging business knowledge, statistical methodologies, data techniques, and machine learning algorithms.
• Build models and data applications to produce actionable insights.
• Create impactful visualizations to deliver simplified insights messaging.
• Collaborate with business and functional stakeholders on analytics initiatives.
• Synthesize business needs and identify optimal data solutions.
• Deliver data insights and recommend next steps to senior stakeholders.

Required Qualifications, Capabilities, and Skills:

• 3 years of data science experience within financial services or commercial banking.
• Strong passion for data and ability to drive analytics capability development using advanced data techniques and statistical methodologies.
• Proficiency in programming languages such as R, Python, SQL.
• Experience with analytical tools like Alteryx, Trifacta, and distributed file systems like Hadoop.
• Familiarity with visualization tools such as Qlik Sense, Tableau, D3.
• Experience with cloud computing platforms, particularly Amazon Web Services.
• Ability to design and execute repeatable and scalable data solutions.
• Strong communication skills to effectively convey insights and recommendations.
• Exposure to designing and building machine learning algorithms.
• Team player with a strong work ethic and professionalism.

Preferred Qualifications, Capabilities, and Skills:

• Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and related methods.
• Experience with building and deploying ML models on cloud platforms such as AWS, including tools like Sagemaker and EKS.
• Advanced knowledge in Reinforcement Learning or Meta Learning.