Principal Associate, Business Analysis

Capital One Capital One · Banking · Bangalore, IN

The role focuses on modernizing Anti-Money Laundering (AML) processes using advanced analytics, statistics, and machine learning models. It involves developing data sourcing, predictive models, products, monitoring, and reporting. The candidate will understand user needs, conduct business analyses, and translate strategy into tangible products and outcomes within the financial services industry, specifically for AML.

What you'd actually do

  1. Develop business strategies that will drive growth, profitability, and competitive success for Capital One in the face of shifting consumer and regulatory demands.
  2. Manage and sequence delivery of business intent, build business requirements and execute against the product strategy.
  3. Work closely with colleagues across Capital One including: Tech, Model Risk, Lines of Business, and others to drive improvement in quality, volume, service, and profitability.
  4. A proven track record of decision making and problem solving based on analytics.
  5. May manage and develop analysts.

Skills

Required

  • Bachelor's Degree in a quantitative field (Statistics, Math, Engineering, Economics, Econometrics, Finance, or Operations Research)
  • At least 4 years of professional work experience in analytics, business analysis, or data-driven product management.
  • Python
  • R
  • SQL
  • relational databases
  • econometric and statistical techniques
  • model design, development, and deployment
  • Fraud / Anti-money Laundering domain

Nice to have

  • Experience in the financial services industry, specifically dealing with cross-LOB operations or customer experience.
  • Proven experience translating high-level business strategy and advanced analytics into tangible products and actionable outcomes.
  • Excellent written and verbal communication skills.

What the JD emphasized

  • quantitative field
  • analytics
  • business analysis
  • data-driven product management
  • Python
  • R
  • SQL
  • relational databases
  • econometric and statistical techniques
  • model design, development, and deployment
  • Fraud / Anti-money Laundering domain

Other signals

  • advanced analytic techniques
  • statistics
  • machine learning models
  • data sourcing
  • predictive models
  • AML products
  • Statistical / ML models
  • advanced analytics