Data Scientist II - AI/ML

Key Skills

Aartificial intelligence, AWS, Azure, Data science techniques, Google Cloud Platform (GCP), Large Language Models - LLMs, Machine learning techniques, PyTorch, TensorFlow

Job Description

Responsibilities

As a Data Scientist II 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.

• Work with key stakeholders within Research and Development as well as 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 Artificial Intelligence techniques.
• Develop an exploratory data analysis approach to verify the initial hypothesis associated with potential Artificial Intelligence/Machine Learning 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.

Qualifications

• Graduate degree in a quantitative discipline such as Computer Science/Engineering, Statistics, Operations Research.
• Experience with the latest techniques in natural language processing including transformers, fine-tuning LLMs, measuring/benchmarking and deploying LLMs with tools such as HuggingFace, Langchain, LLAMA/Mistral and OpenAI, vector databases.
• 4+ years of hands-on data science experience, using typical machine learning and data science tools including pandas, scikit-learn, keras, nltk, and TensorFlow/PyTorch, GPU.
• Experience building production-grade machine learning deployments on AWS, Azure, or GCP.
• Experience working with Apache Spark™ and large-scale distributed datasets.
• Experience communicating technical concepts to non-technical and technical audiences is a plus.
• Passion for collaboration, learn it all mindset and driving value with AI.
• 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.

Mental Requirements:

• Communicating with others to exchange information.
• Assessing the accuracy, neatness, and thoroughness of the work assigned.

Physical Requirements and Working Conditions:

• Remaining in a stationary position, often standing or sitting for prolonged periods.
• Repeating motions that may include the wrists, hands, and/or fingers.
• Must be able to provide a dedicated, secure work area.
• Must be able to provide high-speed internet access/connectivity and office setup and maintenance.
• No adverse environmental conditions expected.

Base compensation ranges from $110,000.00 to $140,000.00. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs.