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Director of Data Science

  • Location

    New York City

  • Sector:

    Financial Services and Banking

  • Job type:


  • Salary:

    $150,000 - $180,000

  • Contact:

    Sasha Stratton

  • Contact email:

  • Salary high:


  • Salary low:


  • Published:

    4 months ago

  • Expiry date:


  • Startdate:


A high growth fintech company that specializes in both small business lending and a specialized digital lending platform is seeking a Director of Data Science to oversee a highly skilled data science team in a fast-paced environment.

The right candidate will have the opportunity to manage a team of data scientists and direct an exciting range of initiatives to drive the digital transformation of the business, optimize risk controls, and utilize artificial intelligence and machine learning to drive profitable growth in both the commercial, small business, and consumer lending space.

Key Responsibilities:

  • Oversee numerous data science projects in Agile mode, while actively managing multiple deadlines
  • Implement data science best practices by enhancing existing framework and establishing a data driven decision making precedent
  • Collaborate cross functionally with both internal and external clients including senior management across multiple different business lines, making recommendations based on data driven insights

Position Requirements:

  • Bachelor's Degree required, advanced degree preferred in a quantitative field such as mathematics, statistics, or economics
  • 3+ years experience with hands-on people management
  • 10+ years of experience in a data science capacity with 5+ years hands on experience with Python programming language, machine learning and natural language processing
  • Experience in financial services, specifically in consumer or small business lending/credit risk management
  • Ability to proactively understand and solve complex business problems, recognize applicable data and perform analysis to then generate and communicate insights to C-suite and non-technical individuals
  • In depth experience with in classic statistical methodologies, big data, supervised and unsupervised learning models