Data Scientist II - Client Analysis

Socure Socure · Vertical AI · United States · Remote · Commercial

Data Scientist II role focused on analyzing customer data, explaining model performance, and building ML models to drive revenue generation for Socure's identity verification and fraud prevention products. The role involves partnering with go-to-market teams, creating data stories, and contributing to the next generation of core models and products.

What you'd actually do

  1. Analyze massive customer data sets, come up with actionable insights, and explain how our models perform and how we can help our customers build optimal risk management policies
  2. Act as a Data Science advocate: deeply understand Socure’s solutions (products, models and algorithms) and customers’ risk challenges to educate and influence from a scientific perspective
  3. Create convincing data stories to showcase how we can help our customers eliminate identity fraud, streamline their end-user experience, and support their risk management efforts holistically
  4. Provide thought leadership and be the customer’s voice for building the next generation of Socure’s core models and product
  5. Create tools and automated workflows to improve the speed, accuracy, and reproducibility of our analysis

Skills

Required

  • Python
  • SQL
  • cloud tools and technologies in AWS, Azure, Databricks, or GCP
  • state-of-the-art supervised and unsupervised machine learning methods
  • telling compelling stories with data (dashboarding, building interactive data stories and actionable presentations)
  • explaining very complex algorithms such as ML/AI models, and analyses to non-technical audiences
  • excellent verbal and written communicator
  • comfortable delivering presentations with clear, complete messages

Nice to have

  • advanced degree
  • experience in identity verification and fraud prevention

What the JD emphasized

  • Build ML models when it is advantageous to explore different approaches over the established models in production
  • extensive theoretical and practical understanding of state-of-the-art supervised and unsupervised machine learning methods
  • experience in identity verification and fraud prevention (strongly preferred)

Other signals

  • customer-facing AI product
  • ML models
  • risk management policies
  • identity verification
  • fraud prevention