Junior Data Scientist

Full-time remote Junior Data Scientist role at Enlyte, focused on finding and delivering practical AI/ML opportunities across a product portfolio. You will collaborate with product managers and engineers to build, evaluate, and deploy machine learning models, including LLM-based generative AI features.

As part of the team, you’ll help turn business needs into production-ready systems by applying strong fundamentals in machine learning, data engineering, and feature engineering. The role includes clear communication expectations, so your work is shared effectively with both technical and non-technical audiences.

What you’ll do

• 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.
• Work with product managers, engineers, and domain experts to solve business problems with ML.
• Follow and contribute to best practices for ML pipelines, model monitoring, and deployment.
• Present findings clearly to technical and non-technical audiences.

What you bring

• MS or PhD in Math, Statistics, Computer Science, or a related field.
• 2+ years of experience building, deploying, and maintaining ML models in production.
• Hands-on experience with data warehouses, feature engineering, ML pipeline automation, and model monitoring.
• Solid knowledge of ML 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.
• Nice to have: Experience with AWS SageMaker.

Tools you’ll use

• Python
• SQL
• AWS SageMaker

Benefits

• Medical
• Dental
• Vision
• Health Savings Accounts / Flexible Spending Accounts
• Life and AD&D Insurance
• 401(k)
• Tuition Reimbursement

Location: Remote (remote)

Compensation: USD 85,000 - 102,000 per year

How you work: You’re a motivated self-starter who can work through ambiguous problems with minimal direction, bring strong analytical skills across ML and data engineering, collaborate effectively on scalable ML pipelines, and stay current with ML research through papers, blogs, and open-source projects.