About the Role
The role involves designing and implementing data models, conducting deep-dive analyses, and translating business problems into data-driven strategies in close collaboration with engineering and product teams.
Responsibilities
- Develop and deploy machine learning models to solve business challenges
- Analyze large-scale datasets to uncover trends and patterns
- Collaborate with product and engineering teams to integrate data solutions
- Design experiments and measure impact of product changes
- Translate business requirements into analytical frameworks
- Present findings to stakeholders using clear visualizations and narratives
- Optimize data pipelines for performance and scalability
- Evaluate model accuracy and iterate on improvements
- Support A/B testing design and interpretation
- Maintain documentation for models and analytical processes
- Identify opportunities for data quality improvements
- Work with distributed systems and cloud-based data platforms
- Ensure compliance with data privacy standards
- Mentor junior team members on technical and analytical approaches
- Stay current with advancements in data science and machine learning
Nice to Have
- PhD in a relevant technical discipline
- Experience with natural language processing
- Background in Bayesian modeling techniques
- Familiarity with real-time data processing systems
- Knowledge of MLOps practices and model monitoring
- Experience in product analytics domains
- Contributions to open-source data science projects
- Published research in data science or machine learning
- Experience with recommendation systems
- Working knowledge of containerization and orchestration tools
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid work model with flexibility based on team and location
Team
Part of a data science team focused on product-driven insights and scalable machine learning solutions
About the Team
- The data science team partners closely with product and engineering to drive data-informed decisions across the organization.
- Projects span model development, causal inference, and scalable analytics infrastructure.
What We Value
- Curiosity and a commitment to rigorous analysis
- Collaboration across disciplines and transparency in process
- Ownership of projects from ideation to production
- Clear communication of complex concepts to non-technical audiences
Available for qualified candidates requiring sponsorship
