Data Scientist I - AI/ML

Cotiviti

1d•Remote

About The Position

The Data Scientist I is focused on machine learning solutions in Healthcare Technology and builds value-oriented, production level machine learning solutions. This is not a research oriented data scientist; instead you will apply your 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 on 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. As a Data Scientist within Cotiviti you will be responsible for delivering solutions that help our clients identify payment integrity issues, reduce the cost of healthcare processes, or improve the quality of healthcare outcomes. You will work as part of a team and will be individually responsible for the delivery of value associated with your projects. You will be expected to follow processes and practices that allow your models to be incorporated into our machine learning platform for production execution and monitoring, however, initial exploratory data analysis allows for more flexible experimentation to discover solutions to the business problems presented.

Requirements

MS or PhD. Degree in relevant discipline (Math, Statistics, Computer Science, Engineering or Health Sciences) or commensurate professional work experience.
1-3 years experience building and deploying Machine learning models.
1-3 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, Keraes, pandas, numpy, scikit-learn, spark, scala, hive, impala.
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.
Mental Requirements: Reasoning and Problem Solving: Ability to actively and skillfully conceptualize, apply, analyze, synthesize, and/or evaluate information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action.
Consulting: Demonstrated ability to make and gain acceptance of data-driven recommendations made to business owners. Strong ability to appropriately summarize and effectively communicate complex concepts & varied data sets to inform stakeholders, gain approval, or prompt actions; Applies to multiple audiences ranging from the analyst to executive level; Includes oral & written communication and multimedia presentation.
Statistical Analysis: Apply statistical methodology to solve business problems; appropriately interprets meaning from results.
Business Knowledge: Good understanding of the tenets of health insurance, the managed care model, industry coding/policy standards, the claim adjudication process, and issues related to fraud waste and abuse. Ability to apply this knowledge to the development & evaluation of new initiatives and support leading the team strategy toward best practices.
Financial Analysis: Ability to understand, generate and evaluate healthcare utilization, unit cost and medical cost trends. This includes understanding levers that effect healthcare cost, such as contracting, networks, policies, benefit structures, and product design. Ability to draw conclusions and make recommendations based on financial data.
Functional Programming: Ability to work with, understand and create object oriented/functional programming solutions using modern application frameworks.
Ability to work independently as well as collaborate as a team.
Flexibility to work with global teams as well geographically dispersed US based teams.
Professional with ability to properly handle confidential information.
Be value-driven, understand that success is based on the impact of your work rather than its complexity or the level of effort.
Ability to handle multiple tasks, prioritize and meet deadlines.
Ability to work within a matrixed organization.
Proficiency in all required skills and competencies above.

Responsibilities

Create actionable and pragmatic data science models with minimal supervision.
Understands business needs and identifies potential use cases in more than one business unit. Works with external partners to develop a minimal viable product to meet those needs while resolving any issues that may arise.
Consistently collaborates with fellow data scientists and frequently interacts with business partners, project managers, cross-functional teams, key stakeholders, and other domains to build analytics capabilities and drive business value.
Continuously work to be updated on the latest developments in machine learning and the healthcare industry. 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.
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.