Machine Learning Operations Lead

RELX•Alpharetta, GA

1d•Remote

About The Position

LexisNexis Risk Solutions, Inc. is seeking a Machine Learning Operations Lead to develop a comprehensive data and analytics cloud migration strategy as part of a broader technology modernization and migration to Microsoft Azure. This role will drive thought leadership and execution of cross-functional analytics teams to enable adoption of data processing and analytical infrastructure in the cloud. The lead will manage, optimize, and operationalize data lakes, data science virtual machines, and other DevOps tools (Github, Jfrog) to enable faster go-to-market capabilities for data science teams. Responsibilities include architecting and building core components of machine learning and data engineering platform infrastructure, developing a comprehensive user, developer, and manager education program to accelerate onboarding into a governed self-service data ecosystem, and defining, managing, and reporting operational SLAs and KPIs for data platforms and solutions. The role involves partnering with data science and IT engineering teams to create high-performance, efficient feature pipelines from backend proprietary data and simplifying the technology stack to sunset legacy applications while minimizing business disruptions. Other duties as needed.

Requirements

Bachelor’s degree (or foreign equivalent) in Applied Computer Science, Computer Engineering, Information Systems, or a related field.
5 years of experience in the job offered or related occupations.
5 years of experience developing architectural design documents for cloud transformation and migration of on-prem services to cloud.
5 years of experience with Azure Fundamentals, Azure Databricks, Azure Data Lake storage, and Azure Compute.
5 years of experience developing orchestration pipelines for data ingest and data transfer.
5 years of experience developing automated data pipelines for batch file transfers across disparate systems.
2 years of experience building custom applications for data and server compute to enable machine learning (ML) enabled computes.
2 years of experience supporting secure, robust, and resilient data and cloud related services.
2 years of experience developing backup systems for non-production and production workloads.

Responsibilities

Develop a comprehensive data and analytics cloud migration strategy for migration to Microsoft Azure.
Drive thought leadership and execution of cross-functional analytics teams for cloud adoption.
Manage, optimize, and operationalize data lakes, data science virtual machines, and DevOps tools (Github, Jfrog).
Architect and build core components of machine learning and data engineering platform infrastructure.
Develop a comprehensive user, developer, and manager education program for a self-service data ecosystem.
Define, manage, and report operational SLAs and KPIs for data platforms and solutions.
Partner with data science and IT engineering teams to create high-performance feature pipelines.
Simplify the technology stack to sunset legacy applications while minimizing business disruptions.