Data Scientist II

Overview

Cotiviti is currently looking to add an industry leading Data Scientist II who is focused on machine learning solutions to join a revolution in Healthcare Technology and build value-oriented, production level machine learning solutions. This role is not for the research-oriented data scientist, but rather should be of interest to those that want to apply their knowledge and experience to real world problems, and seek to utilize Artificial Intelligence and Machine Learning to reduce the cost of healthcare and improve health quality and outcomes. With access to dedicated on premise and cloud based big data solutions, the team can work with a vast amount of structured and unstructured data including claims, membership, physician demographics, medical records and others to begin to solve some of the most pressing healthcare issues of our time. A Data Scientist at Cotiviti will be given the opportunity to work directly with a team of healthcare professionals including analysts, clinicians, coding specialists, auditors and innovators to set aggressive goals and execute them with the team. This is for an ambitious technologist, with the flexibility and personal drive to succeed in a dynamic environment where they are judged based on their direct impact to business outcomes.

Responsibilities

· Work with key stakeholders both within R&D and Operations, along with product management to assess the potential value and risks associated with business problems that have the potential to be solved using machine learning and AI techniques.

· Develop an exploratory data analysis approach to verify the initial hypothesis associated with potential AI/ML use cases.

· Document your approach, thinking and results in standard approaches to allow other data scientists to collaborate with you on this work.

· Prepare your final trained model and develop a validation test set for QA.

· Work with production operations to deploy your model into production and support them in monitoring model performance.

· Participate in other data science teams collaborating with your peers to support their projects

· Participate in knowledge sharing sessions to bring new insights and technologies to the team.

· Participate in design sessions to continuously develop and improve the Cotiviti machine learning platform

· Provide End to End value-based solutions, including data pipeline, model creation and application for end user consumption.

· Complete all responsibilities as outlined in the annual performance review and/or goal setting. 

· Complete all special projects and other duties as assigned. 

· Must be able to perform duties with or without reasonable accommodation. 

 

This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and requirements of the job change. 

Qualifications

Minimum Qualifications

· MS or PhD Degree in relevant discipline (Math, Statistics, Computer Science, Engineering or Health Sciences) or commensurate professional work experience.

· 4-7 years’ experience in advanced analytics

· 3+ years’ experience in working in Big Data environments

· Experience developing machine learning models in an exploratory data analytics environment and working with others to develop production ready versions of the models that are deployed within operational environments

· Experience in using machine learning tools to develop production strength models including, but not limited to, Python, TensorFlow, PyTorch, pandas, numpy, scikit-learn, spark, scala, hive, impala

· Significant experience with SQL, ability to write SQL queries to efficiently extract data from relational databases

· Applied Machine Learning: Application of a variety of machine learning techniques to increase identification of payment integrity issues for our clients, reduce the cost of auditing processes or increase the quality of care and outcomes. Must have implemented machine learning solutions within production environments at scale

· Big Data Analysis: Strong ability to manage and analyze data in a Big Data environment using a variety of

scripts, potentially including but not limited to Scala/Spark and Python as well as Cloud based ML/AI

capabilities.

 

Additional Beneficial Requirements:

· Knowledge or experience of DevOps lifecycle tools like GitHub/Gitlab/BitBucket, Jenkins, Jira

· Experience in natural language processing (NLP) techniques

· Experience in deep learning techniques

· Proficiency in applying various mathe