At Playlist, life's richest moments happen when people step away from screens to move, connect, explore, and play. We're building the definitive platform for intentional living, connecting people with inspiring experiences in fitness, wellness, and beyond. With popular brands like Mindbody and ClassPass, Playlist empowers businesses and individuals, making it effortless for aspirations to become actions. Join us in reshaping technology's role to foster meaningful, real-world connections.
The Role You’ll Play:
As a Data Scientist, you will play a critical role in driving insights and building data products that support large-scale, cross-departmental initiatives. You’ll combine advanced statistical inference, strong engineering skills, and business intuition to create models and tools that improve marketplace efficiency, retention, and growth. This role is not about building models in isolation—it’s about leveraging data science to influence strategy, fuel automation, and deliver measurable business impact.
- Partner with Product, Marketing, Operations, and Finance teams to design and execute analytical solutions that optimize marketplace dynamics, improve unit economics, and uncover opportunities for growth.
- Apply statistical inference and causal modeling to evaluate experiments, quantify business impact, and guide decision-making under uncertainty.
- Design, prototype, and deploy scalable data products such as anomaly detection systems, prioritization algorithms, and similarity/recommendation models.
- Translate complex models and findings into actionable insights and clear recommendations for business stakeholders, including executives.
- Build frameworks for experimentation, measurement, and monitoring that drive iteration and continuous improvement of strategic initiatives.
- Balance hands-on technical work (modeling, coding, automation) with strategic thinking, ensuring data science investments align with business objectives.
- Act as a technical thought partner and mentor for analysts and junior data scientists, raising the bar on scientific rigor and reproducibility.
Experience You Bring:
- 3–5 years of experience in data science, applied statistics, or a related quantitative discipline, ideally within marketplace or platform businesses.
- Strong expertise in statistical inference, causal modeling, and experiment design, with the ability to draw robust conclusions from noisy or incomplete data.
- Proficiency in Python (pandas, NumPy, scikit-learn, statsmodels, PySpark, etc.) and SQL, with experience building and deploying production-grade models.
- Proven track record of designing and delivering data products such as anomaly detectors, ranking/prioritization models, similarity or recommendation systems.
- Familiarity architecting automated pipelines and data flows that support machine learning and analytics at scale.
- Ability to balance technical depth with business acumen—knowing when a simple solution beats a complex one, and when complexity is necessary to win.
- Strong communication and storytelling skills, with the ability to influence non-technical stakeholders and executives.
- Comfort with ambiguity and a fast-paced environment; you thrive on experimentation, iteration, and turning insights into action.
- Familiarity with data visualization and dashboarding tools (e.g., Tableau, Looker, Power BI) to communicate insights effectively.
- Internally competitive and externally collaborative
Sound like the role for you? We’d love to hear from you! Even if you’re not 100% sure about potential fit, we still encourage you to apply. We’re looking for the right person, not the perfect series of checkboxes.
The Company is an Equal Opportunity Employer. We highly value diversity at our company and encourage people of all different backgrounds, experiences, abilities and perspectives to apply. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected characteristics.
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Note: This description outlines key responsibilities but isn’t intended to cover every task or duty. Additional responsibilities may be assigned as needed to support the team and business goals.
