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BluevinePosted 1 month ago

Data Science Lead

HybridBengaluru, Karnataka, India

Part TimeSenior LevelMediumFinance

Job Summary

Design and build response models to predict customer engagement and approval models to estimate product approval likelihood for direct mail and paid media initiatives. Analyze match backs to support marketing decisions, partner with teams to define KPIs, and conduct deep-dive analyses to extract actionable insights. Drive targeting strategy using model outputs, ensure production-ready implementation with software engineering best practices, and monitor performance over time. Collaborate with global stakeholders across Data Science, Marketing, Risk, Product, and Engineering to translate technical findings into business recommendations. This role focuses on enhancing marketing ROI and acquisition growth within Bluevine's small business banking platform.

Required Qualifications

  • 8+ years of experience in data science with a focus on marketing, growth, or customer acquisition
  • Proven track record developing and deploying predictive models for marketing use cases
  • Strong proficiency in Python and ML libraries (e.g., Scikit-learn, XGBoost, PyTorch, or TensorFlow)
  • Advanced SQL skills and experience working with large-scale relational databases
  • Deep understanding of statistical testing, experiment design, and campaign performance analysis
  • Ability to think creatively and move quickly, with a bias for action in a fast-paced environment
  • Exceptional communication skills with the ability to present findings clearly to non-technical stakeholders
  • Master's or Ph.D. in Statistics, Computer Science, Engineering, or a related quantitative field

Desired Qualifications

  • Experience with direct mail targeting, paid media optimization, or acquisition funnel analytics
  • Background in fintech, credit products, or regulated industries
  • Familiarity with MLOps, particularly in the AWS ecosystem, and workflow tools like Apache Airflow
  • Prior work involving uplift modeling, lookalike modeling, or media mix optimization

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