Data Scientist/Machine Learning Specialist - MLOps, Data Science

Machine Learning Engineer - MLOps

Department Overview

Data Science Center of Excellence (CoE) builds and supports the Data Science and Machine Learning Operations (ML Ops) elements of enterprise solutions that involve AI/ML (including Generative AI) capabilities and drive success for organization by creating material savings, efficiencies, and revenue growth opportunities. The team drives business innovation by leveraging emerging AI/ML capabilities and turning them into differentiating business capabilities.

Job Overview

Sustain business value at organization by leading the charge of Run & Sustain support of the Data Science and ML Ops elements of enterprise solutions that currently involve traditional ML and Generative AI capabilities.         

Job Responsibilities

• Possess deep functional and technical understanding of the Machine Learning technologies (Google’s Cloud Platform, custom and COTS-embedded) and leverage it across organization’s large, complex and diverse landscape.
• Business acumen (strong understanding of how business operates, and how to harness data and analytics to meet business needs)
• Ability to develop and deploy advanced analytical models and algorithms that drive profitable growth, predictable patterns and areas of opportunity.
• Maintain a solid understanding of up and downstream impacts implementing solutions using ML technologies.
• Engage early in project efforts in order to analyze current solutions, provide solution options and recommendations, understand business process impact, provide accurate estimates.
• Aide adoption of ML solutions within the business by conducting training, coordinating demos.
• Establish development and delivery best practices in order for ML implementations to minimize rework and maintenance cost. Build ML architectures that work at organization scale.
• Work closely with business stakeholders and members of the build team (Agile Pod) to understand the Run & Sustain needs (including SLA, MTTR) of the solutions and the value that needs to be sustained.
• Leverage documentation created by the build team to gain knowledge of the Data Science and ML Ops elements of the solution.
• Maintain documentation of the solution during Run & Sustain
• Leverage strong understanding and working experience of the ML Ops lifecycle – feature engineering, continuous training, validation, scaling, deployment, HA, DR, monitoring, and feedback loop – to provide Run & Sustain support for ML-based solutions.
• Lead the Service Resolution Team (SRT) and leverage other roles like Data Engineer, Data Analyst and Visualization Engineers, to diagnose and resolve issues and ensure none to minimal impact to users of the solutions and to value of the solution. Priority should be on service restoration.
• Communicate issues with business impact relevant information to business and IT stakeholders of the solutions. Maintain periodic communication on status of the issue and expected resolution time/window.
• Perform Root Cause Analysis (RCA) on the issue and communicate that to the business and IT stakeholders of the solutions.

• Collaborate with the data scientists on model development to containerize and build out the deployment pipelines for new models.
• Collaborate with the data scientists on ML Ops life cycle relevant setup to ensure seamless Run & Sustain of the Data Science and ML Ops elements of the solutions.
• Build infrastructure and/or setup GCP services (like Cloud Functions) to support integration between AI/ML solutions and other solutions.
• Maintain at scale the APIs for consumption of machine learning models.
• Keep abreast of improvements in GCP Vert

Qualifications

• Bachelor's degree preferred or equivalent work experience.
• 7+ years in the distribution or Healthcare industry and deep knowledge of their business practices
• 5+ years of proven Machine Learning experience and involvement in packaged platform delivery and management.
• Strong working knowledge of a variety of machine learning techniques (Regression, Clustering, Decision Tree, Probability Networks, Neural Networks, Bayesian models etc.) along with Generative AI (preferable with Plam 2 or Gemini Pro)
• Experience with Machine Learning and related technologies such as Python, Tensorflow, Torch, Amazon SageMak