Lead Data Scientist- Recommendation Systems

Key Responsibilities

• Design, develop, and optimize end-to-end recommendation systems, from data ingestion to model deployment
• Build, fine-tune, and evaluate recommendation algorithms for scalability and performance
• Collaborate with engineering and product teams to integrate ML solutions into business applications
• Lead and manage projects, ensuring timely delivery of solutions aligned with business objectives
• Provide technical guidance and mentorship to junior data scientists and engineers
• Work directly with clients and stakeholders, demonstrating strong communication and problem-solving skills
• Drive innovation by exploring and implementing new techniques in recommendation systems and AI
• Stay abreast of industry trends and best practices in data science, replenishment optimization, and supply chain management, and leverage this knowledge to drive innovation within the organization
• Collaborate, coach, and learn with a growing team of experienced Data Scientists

Requirements

• 8+ years of overall experience in Data Science / Machine Learning
• 3+ years of hands-on experience in Recommendation Systems
• Proven expertise in recommendation algorithms and MLE practices
• Strong programming skills in Python - Production level coding and SQL
• Experience working with Databricks, Azure, and Google Cloud Platform (GCP)
• Demonstrated leadership and project management experience
• Proactive, accountable, and able to take ownership of complex initiatives
• Exceptional communication and collaboration skills to understand business partner needs and deliver solutions and explain to business stakeholders
• Stakeholder Influence: Ability to lead high-stakes analytics engagements and translate complex data findings into "so-what" insights for senior leadership
• Communication: Exceptional presentation skills, capable of driving strategic conversations and building consensus across diverse organizational teams
• Growth Mindset: A proactive hunger to learn emerging technologies and adapt to the evolving healthcare data landscape