Junior Data Scientist

Enlyte•UNAVAILABLE, UNAVAILABLE

1h•Remote

About The Position

Enlyte is hiring a Junior Data Scientist to identify and deliver AI/ML opportunities across its product portfolio. The role works closely with product managers, engineers, and domain experts to translate business problems into practical solutions, including generative AI and large language model (LLM) features. The position is full-time and remote, and can be located anywhere in the U.S. The successful candidate will help build, evaluate, and deploy production machine learning models; design and test prompts for generative AI features; and contribute to sound practices for ML pipelines, monitoring, and deployment. The role calls for a motivated, self-directed professional who can work through ambiguous problems with minimal direction, communicate findings to technical and non-technical audiences, and stay current with developments in machine learning.

Requirements

Master’s or Ph.D. in mathematics, statistics, computer science, or a related field.
For candidates with a master’s degree, 2 years of professional machine learning experience.
For candidates with a Ph.D., 1 or more years of professional machine learning experience.
At least 2 years of experience building, deploying, and maintaining machine learning models in production.
Hands-on experience with data warehouses, feature engineering, ML pipeline automation, and model monitoring.
Solid knowledge of machine learning algorithms and techniques, with the ability to explain core concepts clearly.
High proficiency in Python and SQL.
Good understanding of data warehousing and ETL processes.
Strong written and verbal communication skills.
Strong analytical skills and solid knowledge of ML methodologies, data engineering, and feature engineering.
Ability to collaborate with engineering teams on scalable ML pipelines and established standards.
Comfort presenting to non-technical stakeholders and sharing knowledge across teams.
Ability and habit of staying current with ML research through papers, blogs, and open-source projects.

Nice To Haves

Experience with AWS SageMaker.

Responsibilities

Identify AI/ML opportunities across products and support implementation efforts.
Design and test prompts for LLM-based generative AI features.
Build, evaluate, and deploy machine learning models in production.
Partner with product managers, engineers, and domain experts to solve business problems with machine learning.
Follow and contribute to best practices for ML pipelines, model monitoring, and deployment.
Present findings clearly to technical and non-technical audiences.