Full-Stack Data Scientist - Styling Algorithms (Remote)
About the Role
We are seeking a Full-Stack Data Scientist - Styling Algorithms (Remote) to join our innovative team. This role offers the opportunity to shape the core recommendation and search systems that power personalized styling experiences. As a Full-Stack Data Scientist, you will be responsible for the full lifecycle of algorithmic solutions, from research and model development to deployment and optimization, directly influencing how stylists engage with inventory and deliver value to millions of clients.
What You'll Do
- Develop and deploy recommendation and search engine models that guide stylists in selecting inventory and delivering client experiences.
- Manage the full lifecycle of algorithmic solutions, including feature engineering, ranking logic, hyperparameter optimization, and business logic implementation.
- Prototype and deploy agentic AI workflows to assist stylists in real time.
- Conduct experiments and analyze results to evaluate feature performance, communicating insights to stakeholders and leadership.
- Collaborate cross-functionally with product, engineering, and design teams to roadmap new features and improvements.
- Leverage historical and real-time client and inventory data to drive evidence-based decision making and business impact.
- Ensure system reliability, scalability, and maintainability while adopting best practices in full-stack machine learning development.
Requirements
- 3+ years of experience leveraging data science to develop customer-focused solutions, with a focus on recommendation or search systems.
- Strong proficiency in Python and SQL for handling large-scale datasets.
- Experience in deploying machine learning models and building data-driven applications.
- Solid understanding of A/B testing methodologies and the ability to balance algorithmic metrics with business metrics.
- Degree in a quantitative field such as Computer Science, Statistics, Mathematics, Physics, or a related discipline.
- Strong communication skills and ability to work effectively with business partners and cross-functional teams.
- Collaborative mindset, growth-oriented, and comfortable experimenting with new techniques and technologies.
- Ability to manage projects end-to-end, from research to deployment, with attention to quality and business impact.
What We Offer
- Competitive salary and equity, aligned with geographic location and experience.
- Eligibility for annual bonuses and restricted stock unit grants.
- Comprehensive health, dental, and vision coverage.
- Flexible, remote-friendly work environment with collaborative team culture.
- Opportunities to work on cutting-edge machine learning and AI applications in a data-rich, creative environment.
- Professional growth and development opportunities with access to mentorship and cross-functional collaboration.
Join us as a Full-Stack Data Scientist and make a significant impact in the world of personalized styling experiences!
This Full-Stack Data Scientist role offers a unique opportunity to work on innovative styling algorithms in a fully remote environment. Competitive salary and equity options enhance the appeal.
About Jobgether
Explore Jobgether careers in 2026 and discover a wide range of job openings, including remote, hybrid, and office roles. Our platform offers advanced filters, application tracking, and valuable company insights to enhance your job search experience. Uncover exciting career opportunities at Jobgether and take the next step towards your dream role today. Join us and shape your future in 2026.
Who Will Succeed Here
Proficiency in Python and SQL, with a strong understanding of libraries such as Pandas and NumPy for data manipulation and analysis, enabling efficient handling of large datasets.
A self-motivated and disciplined work ethic suited for remote environments, with the ability to manage time effectively, prioritize tasks, and collaborate asynchronously with team members across different time zones.
Experience in deploying machine learning models into production, particularly in A/B testing frameworks, demonstrating a mindset focused on iterative improvement and data-driven decision-making.
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