Cotiviti is currently looking to add three (3) industry leading Data Scientists who are 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. Cotiviti’s Machine Learning and Strategic Analytics team consists of data scientists, engineers and data analysts dedicated to developing and implementing robust, scalable, value-based solutions on our proprietary machine learning platform that uses a blend of best in class vendor solutions, together with custom built capabilities where gaps in the market exist. With access to a dedicated Hadoop based multi-node cluster 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 guts, 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 in the Machine Learning and Strategic Analytics team 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, but 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.
- Work with a Data Science Team Lead, data science management, business operations and 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 with the team lead to verify the initial hypothesis associated with potential AI/ML use cases.
- Once verified, develop in depth EDA to create models that meet or exceed the thresholds required to deliver on the use case.
- Develop requirements for features required in your final model and interact with the feature library team to get your features built into the library.
- 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 using features within the feature library 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
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 for our client’s members. Must have implemented machine learning solutions within production environments at scale
Big Data Analysis: Strong ability to manage and analyze data in a Hadoop environment using a variety of scripts, including Scala/Spark, Python, and others.
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: Applies statistical methodology to solve business problems; appropriately interprets meaning from results
Business Knowledge: Good understanding of the tenets of health insurance a benefit , 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
- MS or PhD. Degree in relevant discipline (Math, Statistics, Computer Science, Engineering or Health Sciences)
- 3+ years’ experience in advanced analytics and applied machine learning solutions
- 3+ years’ experience in working in Big Data environments, specifically Hadoop
- Experience developing machine learning models in an exploratory data analysis environment then 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, pandas, numpy, scikit-learn, spark, scala, hive, and impala
- A working knowledge of SQL, able to write SQL queries to efficiently extract data from relational databases
- 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
Additional Beneficial Requirements:
- Knowledge of machine learning tools such as DataRobot, H2O, ML Flow
- 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 mathematical and statistical models to include, but not limited to: Random Forest, Gradient Boosting, Time Series, Support Vector Machines, Collaborative Filtering, and Unsupervised Clustering
- Experience or knowledge of the health insurance industry in the U.S.
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