Data Scientist – Production Machine Learning

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Lifted

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2018

🏥 Healthcare

👥 HR Tech

📋 Compliance

💰 $6.2M Series A - Lifted on 2021-06

Healthcare • HR Tech • Compliance

Lifted is a platform for health and social care providers that streamlines recruiting, onboarding, sponsorship and ongoing compliance. The product offers access to pre-screened care workers, automated background and right-to-work checks, digital training and audit-ready records, Certificate of Sponsorship and visa application support, and real-time compliance dashboards and alerts. It is built to help care employers hire, sponsor and stay audit-ready with embedded immigration and compliance workflows.

📋 Description

• Develop, deploy, and maintain machine learning models in production environments. • Perform exploratory data analysis to identify patterns, opportunities, and modeling approaches. • Conduct feature engineering and prepare data for machine learning workflows. • Build and improve data pipelines supporting model development, deployment, and maintenance. • Monitor production models and help address performance or operational issues. • Collaborate with engineering and business stakeholders to translate business needs into scalable machine learning solutions. • Use version-control and collaborative development practices to manage production code. • Work independently while communicating progress, risks, and technical findings clearly. • Contribute to LLM- or AI-agent-based capabilities where applicable.

🎯 Requirements

• At least 2 years of experience building and maintaining production machine learning models. • Strong Python programming skills. • Advanced SQL skills. • Experience deploying machine learning models into production. • Experience monitoring or maintaining models after production deployment. • Experience with AWS SageMaker or another enterprise machine learning platform, such as Vertex AI or Azure Machine Learning. • Experience supporting production machine learning pipelines. • Experience with Git or another version-control system. • Experience performing exploratory data analysis and feature engineering. • Experience building or improving data pipelines that support machine learning workflows. • Ability to work independently in production environments. • Strong communication and cross-functional collaboration skills.

🏖️ Benefits

• Remote work options