Lead Data Scientist

SagilityGeorgia Center, VT
$141,107 - $147,000Remote

About The Position

Sagility combines industry-leading technology and transformation-driven BPM services with decades of healthcare domain expertise to help clients draw closer to their members. The company optimizes the entire member/patient experience through service offerings for clinical, case management, member engagement, provider solutions, payment integrity, claims cost containment, and analytics. Sagility has more than 25,000 employees across 5 countries.

Requirements

  • Must have a Bachelor’s degree or foreign equivalent in Engineering (Any), Data Analytics, or a related field plus five (5) years of experience in the position offered, as a Data Analyst, or a related position.
  • Must have three (3) years of experience with all of the following: Functional domain experience in healthcare; Forming data-driven insights, designing and building analytical solutions, and validating model performance; Developing Machine Learning/AI models, statistical analysis, and data explorative techniques using Python, SAS, PowerBI, Oracle DB, and Excel; Bridging analytical insights to visualizations using dashboards, PowerBI, or Tableau; Structuring and building data-centric solutions; Building predictive models that improve savings and operational efficiency in Payment integrity (pre- and post-payment cycles); Researching and developing machine learning models to identify social determinants for population health management; Determining population health performance by coding and computing HEDIS measures (NCQA), while translating requirements and data-driven insights for annual NCQA certification; and Performing total cost of care assessments for aging populations and determining individuals at high risk for long term care utilization.

Responsibilities

  • Collaborate with business stakeholders to understand and analyze business requirements to develop solutions that will assist management with decision-making.
  • Gather and analyze data from various disparate sources, which may include RDBMS, Web APIs or any other customized system.
  • Organize large datasets to extract actionable insights and innovative ways to integrate datasets.
  • Perform exploratory data analysis to analyze datasets and make broad conclusions based on initial evaluations.
  • Evaluate various options in terms of appropriate modelling technique/algorithm and apply the best fit for the overall business and technical environment.
  • Apply predictive modeling and machine learning to improve customer experiences, revenue generation, fraud detection, and cost saving, as well as other business benefits.
  • Design and build meaningful data visualizations that explain model outcomes and link the findings with insights to describe business impact in an effective manner.
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