Quant Analytics [Multiple Positions Available]

JPMorganChase•Jersey City, WA

4d•Onsite

About The Position

The Quant Analytics role involves delivering analytical approaches to enhance fraud and scam prevention efforts. This includes developing and maintaining a data strategy roadmap, collaborating with stakeholders to translate business priorities into data initiatives, and partnering with executive leadership to articulate the vision for data-driven decision-making. The role also focuses on driving best practices in data management and analytics, monitoring and optimizing data processes, and managing requirements with a high tolerance for ambiguity. The Quant Analyst will guide clients, establish guidelines for consistency and quality, and collaborate with various teams to integrate data discoveries into operational capabilities.

Requirements

Master's degree in Information Technology, Analytics, Mathematics, or other related quantitative field or related field of study plus 3 years of experience in the job offered or as Quant Analytics, Data Owners, Quant Analyst, Analyst, Product Management, or related occupation.
Alternatively, a Bachelor's degree in Information Technology, Analytics, Mathematics, or other related quantitative field or related field of study plus 5 years of experience in the job offered or as Quant Analytics, Data Owners, Quant Analyst, Analyst, Product Management, or related occupation.
Applying advanced analytics techniques using SQL, Python, or R to extract actionable insights from large-scale digital datasets, including conducting exploratory data analysis, and visualizing results to inform business decisions and strategy.
Querying and wrangling large datasets within cloud-based data platforms including AWS, Hadoop, and Spark, including developing complex queries, performing data cleansing, transformation, and preparing data for analytics to support fraud detection and customer protection initiatives.
Designing and automating business-critical dashboards and reports using Tableau and Excel including Data Visualization via Tableau and Excel, dynamic filtering, and KPI tracking.
Translating complex analytical findings into compelling PowerPoint presentations that drive strategic decision-making and clearly communicate actionable insights to business and executive audiences.
Utilizing digital analytics to capture, interpret, and visualize user behavior data, translating complex digital metrics into actionable insights that drive product and security enhancements.
Collaborating with cross-functional teams including data engineers, product managers, and business stakeholders to define key performance indicators (KPIs), develop measurement frameworks, and align analytics initiatives with strategic business objectives.
Implementing best practices for data stewardship across diverse data sources including Snowflake, Teradata, Data Lake, and business units.
Communicating statistical concepts and analytical findings to both technical and non-technical audiences, including senior leadership, through clear presentations, data storytelling, and well-structured documentation.
Driving analytics requirements and ambiguous problem statements, quickly learning new cloud-based computing and analytics platform including AWS and Databricks, and maintaining focus on delivering high-impact, data-driven solutions.

Responsibilities

Deliver analytical approaches to identify opportunities for enhancing fraud and scam prevention efforts.
Develop and maintain a comprehensive data strategy roadmap in collaboration with Data Owners, Tech, and Product Owners.
Collaborate with stakeholders to understand business priorities and translate them into actionable data initiatives.
Partner with executive leadership to articulate the vision, goals, and roadmap for data-driven decision-making.
Drive the adoption of best practices in data management and analytics through training, mentoring, and knowledge sharing.
Continually monitor and optimize data processes, analytical models and infrastructure.
Manage disparate requirements with a high tolerance for ambiguity.
Guide clients keeping the ultimate project objective in mind.
Establish and adhere to guidelines ensuring consistency and high quality of presentation materials.
Collaborate with unit managers, end users, developers, and other stakeholders to integrate data discoveries and processes into operational capabilities.