Data Science Analyst
Denver, CO
Full Time

Data Science Analyst

Position Summary

Payfone Data Analysts play a critical role in building the next generation of authentication products. You will work closely with engineering, product and sales teams to refine products that directly impact customer experiences. You’ll play a pivotal role in shaping the future of identity verification and help us achieve our mission of creating a state-of-the-art frictionless authentication platform.

What you’ll be doing:

  • Lead customer data studies, providing valuable insight for sales during pre-sales function
  • Assist in prototyping new analytics & machine learning models that improve both our insights and the product directly
  • Work with product and engineering teams to aid testing of new product ideas and analyze results to provide actionable recommendations
  • Perform deep analysis and build models to understand customer and product behavior, and extract key insights that impact product decisions
  • Synthesize data learnings into compelling stories and communicate to stakeholders
  • Monitor and help refine metrics for product efficacy and customer success
  • Work with the broader Data team to find ways to scale our insights through better systems and automation
  • Act as a strategic partner to product and engineering leaders to help prioritize opportunities and inform product strategy

What we look for in you:

  • Demonstrate our core cultural values: clear communication, positive energy, continuous learning, and efficient execution
  • Understanding of statistical concepts and experience in applying them
  • Experience in R or Python.
  • Experience in a scripting language (C-shell or Bash).
  • Be able to independently create plans for analytics projects and build collaboration within the team
  • Bachelors Degree with 2+ years relevant experience or Masters in related field
  • Working knowledge of basic data science concepts such as:

-Setting up a supervised modeling problem

-Evaluating model performance through common metrics such as AUC, GINI, KS, etc.

-Advantages and disadvantages of various supervised modeling techniques such as Linear/Logistic Regression, Decision Trees, Ensemble Modeling, etc.

-Unsupervised modeling techniques such as K-means, PCA. LDA, etc are a plus

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