Corporate Finance – Data Science Product Associate

About the role

Join a team building modern data and analytics products that power Finance at scale. You’ll partner with product, data science, and engineering to deliver user-centric capabilities that improve data quality and accelerate trusted reporting. Your work will shape model-powered features, intuitive dashboards, and controls that leaders rely on.

Job Responsibilities

• Translate business problems into analytical requirements and clear acceptance criteria; refine epics and write user stories that maximize value.
• Analyze product usage, customer behavior, and model performance to surface insights that inform prioritization and roadmap decisions.
• Build executive-ready dashboards and narratives; design A/B tests and pilots, define success metrics, and evaluate outcomes including return on investment.
• Partner with engineering on data validation, lineage, documentation, and control alignment; ensure compliance with privacy, security, and model risk requirements.
• Maintain and prioritize a backlog of data enhancements aligned to business outcomes; manage delivery using Agile practices and tooling.
• Facilitate cross-functional forums; synthesize feedback into clear recommendations and communicate complex findings in business language.
• Standardize reporting, create playbooks, and streamline processes for repeatable, scalable insights delivery.
• Support development and testing of AI and machine learning models and data controls to improve data quality and operational efficiency.

Required Qualifications, Capabilities, And Skills

• Bachelor’s degree in a quantitative field (for example, computer science, statistics) and a minimum of four years in product analytics, business analytics, or data science within a digital or product environment.
• Proficiency in SQL and a data visualization tool; familiarity with cloud data platforms; hands-on experience with Amazon Web Services and Databricks.
• Proficiency in Python or R for exploratory analysis and model evaluation; experience with time series analysis and modeling, and training or fine-tuning machine learning models.
• Experience with experimentation (A/B testing), cohort analysis, key performance indicators (KPIs), and measurement plans for model-powered features.
• Able to manage multiple workstreams under tight deadlines; strong analytical, problem-solving, and collaboration skills to influence decisions across business and technology.
• In-depth knowledge of data and business intelligence concepts, including extract, transform, load (ETL), data modeling, and reporting automation.
• Strong storytelling skills with the ability to craft clear, concise narratives from complex data for executive and non-technical audiences.

Preferred Qualifications, Capabilities, And Skills

• Experience with Agile delivery methodologies and tools to manage both technical and functional work.
• Exposure to machine learning productization, including model monitoring, drift detection, and feature performance measurement.
• Knowledge of banking products such as loans, deposits, cash management, derivatives, and securities from both technical and business perspectives.
• Awareness of user interface and user experience (UI/UX) principles; experience improving interaction by integrating user needs with technical functionality.
• Experience with Jira and Confluence.
• Familiarity with model risk governance and documentation standards.