Lead Data Science Engineer - Remote (Req. #748)

Founded in 1994 and celebrating 30 years in business, Mindex is a software development company with a rich history of demonstrated software and product development success. We specialize in agile software development, cloud professional services, and creating our own innovative products. We are proud to be recognized as the #1 Software Developer in the 2023 RBJ's Book of Lists and ranked 27th in Rochester Chamber’s Top 100 Companies. Additionally, we have maintained our certification as a Great Place to Work for consecutive years in a row. Our list of satisfied clients and #ROCstar employees are both rapidly growing— Are you next to join our team?

Mindex’s Software Development division is the go-to software developer for enterprise organizations looking to engage teams of skilled technical resources to help them plan, navigate, and execute through the full software development lifecycle.  

We are seeking a highly skilled and motivated Lead Data Science Engineer to join our AI Platform team.

Essential Functions
This role will be pivotal in building and scaling our data-driven products and services. You will transform raw data into actionable intelligence, develop and deploy robust machine learning models, and help establish foundational MLOps workflows on modern cloud infrastructure.

Key Responsibilities:

• Design and implement scalable data pipelines to ingest, process, and transform large datasets (structured & unstructured).
• Develop, validate, and optimize supervised and unsupervised machine learning models leveraging Python, SQL, and modern libraries.
• Conduct feature engineering, model selection, and statistical modeling to deliver high-impact solutions.
• Build and expose model APIs or containerized workflows for seamless integration and deployment in production environments.
• Apply MLOps best practices to model versioning, testing, monitoring, and deployment.
• Work with Big Data technologies such as Databricks and Snowflake to unlock analytics at scale.
• Orchestrate complex workflows using tools like Airflow or Dagster for automation and reliability.
• Collaborate with AI teams to refine prompt engineering and leverage AI tooling for model fine-tuning and augmentation.
• Maintain familiarity with leading cloud platforms (AWS, Azure, GCP) for model training, deployment, and infrastructure management.
• Partner with product, engineering, and business teams to translate requirements into technical solutions.