About the role
Company Overview
At Enlyte, we combine innovative technology, clinical expertise, and human compassion to help people recover after workplace injuries or auto accidents. We support their journey back to health and wellness through our industry-leading solutions and services. Whether you're supporting a Fortune 500 client or a local business, developing cutting-edge technology, or providing clinical services you'll work alongside dedicated professionals who share your commitment to excellence and make a meaningful impact. Join us in fueling our mission to protect dreams and restore lives, while building your career in an environment that values collaboration, innovation, and personal growth.
Be part of a team that makes a real difference.
Job Description
This is a full-time remote position that can be located anywhere in the U.S.
Mitchell is looking for a Data Scientist to identify and deliver AI/ML opportunities across our product portfolio. You'll work closely with product managers and engineers to turn business problems into working solutions — including generative AI and large language models (LLMs).
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 ML.
Follow and contribute to best practices for ML pipelines, model monitoring, and deployment.
Present findings clearly to both technical and non-technical audiences.
You are a motivated self-starter who can work through ambiguous problems with minimal direction. You bring:
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.
Clear communication skills — comfortable presenting to non-technical stakeholders and sharing knowledge across teams.
A habit of staying current with ML research through papers, blogs, and open-source projects.
Qualifications
MS or PhD in Math, Statistics, Computer Science, or a related field.
MS: 2 years of professional ML experience
PhD: 1+ years of professional ML experience
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.
Benefits
We’re committed to supporting your ultimate well-being through our total compensation package offerings that support your health, wealth and self. These offerings include Medical, Dental, Vision, Health Savings Accounts / Flexible Spending Accounts, Life and AD&D Insurance, 401(k), Tuition Reimbursement, and an array of resources that encourage a lifetime of healthier living. Benefits eligibility may differ depending on full-time or part-time status. Compensation depends on the applicable US geographic market. The expected base pay for this position ranges from $85,000 - $100,000 annually, and will be based on a number of additional factors including skills, experience, and education.
The Company is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender, gender identity, sexual orientation, age, status as a protected veteran, among other things, or status as a qualified individual with disability.
Don’t meet every single requirement? Studies have shown that women and underrepresented minorities are less likely to apply to jobs unless they meet every single qualification. We are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.
Originally posted on Himalayas