Credit Risk Data Scientist


About us

Zolve’s mission is simple: we want to make financial products accessible to everyone. Zolve offers the ambitious a plethora of products to simplify banking in the US, such as:

  • A Bank Account you can create from anywhere in the world in under five minutes
  • A high-limit Credit Card that helps build a healthy credit score
  • Easy, lightning-fast, cross-border money transfers with the best rates in the market

Zolve is expanding its horizons, and we’re excited to open our doors to everyone expanding theirs. We believe: that if space tourism is real, so is reaching for the stars..

Responsibilities

    • You will be a key member of the Credit & Risk team at Zolve. The incumbent will be reporting into the Head of Credit & Risk at Zolve
    • The role requires working with large data sets using quantitative techniques and building complex statistical models that learn from big data. Data sets will include traditional transactional data, customer attributes obtained at the time of onboarding and alternate data sourced from non-traditional data vendors.
    • Building propensity models for underwriting, collections, Cross sell, loss projections, budgeting etc.
    • Taking care of end-to-end delivery in predictive modelling, such as Data visualization, Data quality check -Cleaning, Aggregation, Data Segmentation
    • Working on cohort identification, development and validation
    • Working on Risk Based Pricing and Implementation
    • Understanding product and business offerings and give data driven insights to build risk strategies, acquisition strategies, product strategies, and overall business & market dynamics for portfolio growth.
    • Support day to day operational risk management and credit life cycle management.
    • We have a highly collaborative process and you are required to work across multiple teams and functions for developing cutting edge, creative and advanced analytic solutions and processes.

Requirements

  • Candidate with 5+ years of analytical experience in applying statistical solutions to business problems
  • Bachelors or Post Graduate degree (Masters or Ph.D.) in Quantitative field such as Statistics, Mathematics or equivalent experience preferred
  • Hands on experience with one or more data analytics/programming tools such as SAS/Salford SPM/Hadoop/R/SQL/Python/Hive
  • Proficiency in some of the following statistical techniques: Linear & Logistic Regression, Decision Trees, Random Forests, Markov Chains, Support Vector Machines, Neural Networks, Clustering, Principal Component Analysis, Factor analysis etc
  • Experience in predictive data Modelling using ML Techniques, ability to interpret complex and large data sets
  • Experience in banking / fintech / consumer lending industry preferred