Senior Data Scientist (Recommendation Systems)Full Time | Santa Monica, CALocation: Remote (United States)
Summary:
Glo is seeking a visionary yet hands-on Senior Data Scientist - Recommendation Systems to lead the design and development of a cutting-edge recommendation engine. This system will revolutionize how our members discover and engage with personalized yoga, meditation, and Pilates content, aligning with Glo's mission to help people consistently practice.
This role combines deep expertise in machine learning and AI with strategic thinking and collaboration. The ideal candidate will contribute to refining data infrastructure while designing advanced models to deliver a world-class personalization experience. You'll partner closely with leadership, product managers, engineers, data scientists, and content creators to ensure the recommendation engine delivers high-quality, impactful experiences for our members.
Responsibilities:Strategize and Lead: Drive the vision, strategy, and execution of Glo's recommendation engine, building the framework and roadmap to optimize user engagement and content discovery.Ground-Up Development: Design, prototype, and implement machine learning models for recommendation systems, including collaborative filtering, content-based filtering, and hybrid approaches, tailored to Glo's unique content ecosystem.Collaborate Across Teams: Partner with leadership, product managers, engineers, and content creators to align technical solutions with user experience goals.Data-Driven Innovation: Analyze large datasets to uncover insights about user behavior and preferences, using these insights to refine recommendation algorithms and drive strategic decisions.Performance Optimization: Ensure the scalability, efficiency, and effectiveness of recommendation models through continuous testing, tuning, and performance monitoring.Technical Leadership: Mentor and provide guidance to team members on best practices in machine learning and data science, fostering an environment of innovation.Measure Success: Define and monitor key metrics (e.g., precision, recall, engagement) to evaluate and iterate on the system's performance.Build for the Future: Contribute to the long-term vision of Glo's personalization capabilities, ensuring the recommendation engine evolves alongside user needs and company goals. Qualifications:Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or a related field.5+ years of experience in data science roles with a focus on building recommendation systems or personalization engines.Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) and statistical modeling techniques.Strong programming skills in Python or R, with experience using SQL and big data tools (e.g., Spark, Hadoop, Databricks). Familiarity with AWS cloud services is a plus.Experience building recommendation systems at scale, including collaborative filtering, neural networks, or hybrid techniques.Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders.Familiarity with behavioral science concepts and their application in personalization is a strong plus. Cultural Values:Start with the whyDrive changePractice resilienceEmbrace progress over perfectionCommunicate openlyWin together Employment Information:
This position is fully remote for anyone living in California, Arkansas, Minnesota, Florida, Tennessee, or Texas.
Some travel may be required for in-person collaboration.
Compensation: $150,000 - $180,000/year + equity.
The above job description is meant to describe the general nature and level of work being performed. It is not an exhaustive list of all responsibilities, duties and skills required for the position. Employees will be required to follow any other job-related instructions and to perform other job-related duties requested by their supervisor in compliance with Federal and State Laws.
All job requirements are subject to possible modification to reasonably accommodate individuals with disabilities. Some requirements may exclude individuals who pose a direct threat or significant risk to the health and safety of themselves or other employees.
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