AI Machine Learning Scientist

Elevance Health•Indianapolis, IN

2d•Remote

About The Position

The AI Machine Learning Scientist is responsible for developing experimental and analytic plans for machine learning algorithms and data modeling processes, establish strong baselines, and accurately determine causal relationships. Leverage Artificial Intelligence (AI) scientific and statistical methods to assist with product creation, development, and improvement. Engage with product teams and business stakeholders to align on project objectives and ensure AI models meet business goals. Lead initiatives for developing Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLM) LLM models. Play a critical role in steering the strategic direction for ML, NLP, LLM, and algorithm development within the company’s AI/ML team, collaborating with distinguished experts in AI/ML modeling, ML engineering, data science, and data engineering. Define and articulate roadmaps for AI/ML model development, acting as a key figure in AI-driven transformation to deliver value internally and to customers. Design and develop customized ML, GenAI, NLP, and LLM models for both batch and stream processing-based AI/ML pipelines. This includes handling data ingestion, preprocessing, search and retrieval, Retrieval Augmented Generation (RAG), and ensuring that complete solutions meet all technical and business requirements, as well as Service Level Agreement (SLA) specifications. Work closely with MLOps, machine learning engineers, and software engineers to ensure seamless integration of machine learning models into production systems. Collaborate with the MLOps team to create and maintain strong evaluation solutions and tools that assess model performance, accuracy, consistency, and reliability during development and UAT. Mentor junior team members and influence the strategic direction of our ML and data science projects.

Requirements

Bachelor’s degree in a highly quantitative field: Computer Science, Machine Learning, Operational Research, Analytics, Statistics, Mathematics, or a related field of study.
Four (4) years of Information Technology (IT) experience, or related experience.
Four (4) years of required Information Technology (IT), or related experience must include: Experience with Machine Learning techniques including supervised learning (linear regression and classification) and unsupervised learning (clustering).
Experience with Deep Learning techniques including convolutional neural networks, recurrent neural networks, and reinforcement learning.
Experience using Python programming language.
Experience with big data technologies including Hadoop, Apache Spark, and AWS.
Experience with relational database management and SQL.
Experience with Natural Language Processing (NLP) techniques including sentiment analysis and text classification.
Experience with statistical analysis and application of statistical methods.

Nice To Haves

Master’s degree in a highly quantitative field: Computer Science, Machine Learning, Operational Research, Analytics, Statistics, Mathematics, or a related field of study and Two (2) years of Information Technology (IT) experience, or related.

Responsibilities

Develop experimental and analytic plans for machine learning algorithms and data modeling processes.
Establish strong baselines and accurately determine causal relationships.
Leverage Artificial Intelligence (AI) scientific and statistical methods to assist with product creation, development, and improvement.
Engage with product teams and business stakeholders to align on project objectives and ensure AI models meet business goals.
Lead initiatives for developing Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLM) LLM models.
Steer the strategic direction for ML, NLP, LLM, and algorithm development within the company’s AI/ML team.
Collaborate with experts in AI/ML modeling, ML engineering, data science, and data engineering.
Define and articulate roadmaps for AI/ML model development.
Design and develop customized ML, GenAI, NLP, and LLM models for both batch and stream processing-based AI/ML pipelines.
Handle data ingestion, preprocessing, search and retrieval, Retrieval Augmented Generation (RAG).
Ensure complete solutions meet all technical and business requirements, as well as Service Level Agreement (SLA) specifications.
Work closely with MLOps, machine learning engineers, and software engineers to ensure seamless integration of machine learning models into production systems.
Collaborate with the MLOps team to create and maintain strong evaluation solutions and tools that assess model performance, accuracy, consistency, and reliability during development and UAT.
Mentor junior team members and influence the strategic direction of our ML and data science projects.