Leverage your problem-solving skills to thrive in a fast-paced environment and drive customer-centric strategies. As a leader in solutioning, collaborate closely with the Sales teams to deliver tailor-made product solutions that meet clients’ needs.

 

As a Vice President Product Solutions Manager in Chief Data and Analytics Office (CDAO), you are an integral part of a team that defines and configures complex solutions for key client relationships and prospect opportunities in partnership with Sales. You are responsible for acting as the voice of the customer by understanding their needs and communicating feedback to the Product teams.

 

As the Solutions Engineering VP within the firmwide Chief Data and Analytics Office (CDAO), you will be part of a team that advises firmwide teams in designing and integrating AI solutions within their application architecture. Your role involves learning and supporting the team in providing guidance and empowering clients to accelerate delivery of AI value. With a willingness to learn and develop communication and stakeholder management skills, you will contribute to building collaborative relationships with Product, Engineering, and Architecture teams across JPM Lines of Business, Corporate Functions, and Internal Fusion Product and Engineering Teams to drive priority outcomes.

 

 

Job responsibilities 

Leads solutioning and the adoption of existing and upcoming client-facing products and capabilities while defining and configuring optimal solutions that address clients’ needs and objectives.Serves as a subject matter expert on a defined set of products and capabilities with a deep understanding of our clients’ needs and current industry trends.Supports Sales in pricing, pipeline planning, account planning, and upskilling the team on product knowledge by collaborating on training and collateral materials.Engages with client teams to better understand pain points and refine solutions while regularly communicating critical client feedback to Product teams to inform the strategic product roadmap.Assist in providing technical support to empower internal clients in designing and deploying Machine Learning, Gen AI, and Agentic systems.Learn to leverage ML experience to guide internal clients in their application development journey, considering performance, evaluation, monitoring, resiliency, and controls.Develop an understanding of firmwide standards for Model and Software Development Lifecycles and Information Security Controls.Learn to anticipate risks associated with machine learning solutions and prediction/classification systems and support strategies for mitigation.Support transparent cross-functional partnerships with product management, engineering, and client engagement, and learn to influence peers and team members to uphold these standards.Assist in presentations, whiteboard sessions, and technical workshops, learning to effectively communicate the value, differentiators, and capabilities of our solutions.Develop skills to communicate complex issues clearly and credibly to senior management and stakeholders, support the team in fostering innovation and collaboration among Solutions Engineers and data scientists.

 

Required qualifications, capabilities, and skills 

5+ years of experience or equivalent expertise in problem-solving across multiple teams and a cluster of productsExtensive experience working in a sales cycle and engaging with clients on a regular basis.Experience modifying preconfigured solutions to meet complex problems.Demonstrated prior experience working in a highly matrixed and complex organization.Experience in programming languages such as Python, R, or Java, with a focus on learning Python.Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, and industry-leading GenAI models.Experience in data manipulation and analysis using tools like Pandas, NumPy, and SQL.Extensive experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization.Proficient in using version control systems like Git.Good foundation in mathematics and statistics, including knowledge of linear algebra, calculus, probability, and statistical methods.

 

 

Preferred qualifications, capabilities, and skills 

Exposure to building and consuming APIs and working with microservices architecture.Familiarity with big data technologies like Hadoop, Spark, or Apache Kafka for handling large datasets.Understanding DevOps and MLOps practices for automating and streamlining the machine learning lifecycle. Exposure to developing, training, and deploying machine learning models, Gen AI models, and knowledge of model evaluation and optimization techniques.