ML Ops Engineer

Responsibilities:

As a Machine Learning (ML) Operations Software Engineer within the Operational Artificial Intelligence team you will be responsible for deploying and maintaining machine learning systems in production reliably and efficiently. You will work as part of a team and will be responsible for managing and monitoring production ML systems for their operations, performance and effectiveness. You will be expected to follow processes and practices that allow for scalable deployment of ML models and solutions to production that automate various stages of machine learning life cycle from data collection, preparation, transformation, train, validation, serve and monitor.

• Design and build scalable data transformation and machine learning pipelines and workflows for production deployments.

• Work closely with Data scientists, Engineers to set and build policies, practices and governance for systematically managing machine learning and artificial intelligence solutions throughout their life cycle.

• Utilize benchmarks, metrics and monitoring to measure and improve performance of machine learning models.

• Research, design, implement and validate various algorithms to analyze diverse sources of data achieve targeted outcomes.

• Develop and implement automation tasks in addition to test automation.

• Work with Data Scientists, Engineers and Operations for deployment of ML models to production and support them in monitoring model performance.

• Participate in data science and engineering teams in collaborating with them 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 and repeatable pipeline solutions, including data pipeline, model creation and monitoring for end user consumption.

Minimum Qualifications

• BS, 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

• 4+ 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, Keras, pandas, numpy, scikit-learn, spark, scala, hive, impala

• Significant experience with SQL, ability 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

• Experience with business intelligence (data visualization and dashboarding, Tableau, Superset etc)

• 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 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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