Job Description

We have an exciting opportunity for you to enhance your career in Data & Analytics, contributing to innovative banking solutions.

As a Data Scientist Lead at JPMorgan Chase within the Data and Analytics team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objective

Job Responsibilities:

• Serve as the subject matter expert on machine learning techniques and optimizations to drive sales enablement and performance analytics.
• Design, develop, and enhance ML workflows to support sales strategies, customer segmentation, campaign effectiveness, and branch performance optimization.
• Conduct experiments using advanced ML and statistical methods, analyze results, and tune models to deliver actionable insights for sales and customer engagement.
• Actively engage in hands-on coding, converting experimental results into robust, scalable production solutions.
• Take full ownership of the code development lifecycle in Python, from proof of concept and experimentation to production-ready solutions.
• Collaborate with business partners, product managers, and technology teams to integrate ML solutions into branch operations and sales processes.

Required Qualifications, Capabilities, and Skills

• Masters in Computer Science, Statistics, Mathematics, Data Science, or a related field, with at least 8 years of applied data science and machine learning experience.
• Hands on experience in one or more programming languages such as Python, R, or Java. Intermediate to advanced Python proficiency is required.
• Proven experience applying machine learning techniques to solve business problems in sales analytics, customer analytics, or financial services.
• Strong background in statistical modeling, data mining, and machine learning methods (e.g., regression, classification, clustering, time series analysis, ensemble methods).
• Experience with machine learning and deep learning frameworks such as scikit-learn, PyTorch, or TensorFlow.
• Ability to independently manage tasks and projects through to completion with limited supervision.
• Excellent communication skills, with the ability to present complex ML concepts to non-technical stakeholders.
• Strong attention to detail and a collaborative, team-oriented mindset.

Preferred Qualifications, Capabilities, and Skills

• Experience in sales enablement analytics, marketing analytics, or performance analytics within banking or financial services.
• In-depth understanding of advanced ML methodologies such as customer segmentation, campaign attribution, recommender systems, uplift modeling, and graph analytics.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and tools for building and deploying ML models (e.g., Sagemaker, Databricks, MLflow).
• Familiarity with big data technologies (e.g., Spark, Hadoop) and data engineering best practices.
• Software development experience is a plus.