Your Opportunity
As a Lead Data Scientist, you will be a technical cornerstone of the analytics organization, driving the development of advanced data products and machine learning solutions that power strategic decision-making. You will work at the intersection of product, engineering, and strategy to uncover deep insights from our vast telemetry data. In this high-impact individual contributor role, you will set the standard for technical excellence, architect scalable data science frameworks, and mentor junior data scientists, all while solving complex problems in the observability space.
What you'll do
- Lead the design, development, and deployment of advanced ML models and predictive algorithms to solve critical business problems, such as churn prediction, user segmentation, and product usage forecasting.
- Serve as a technical subject matter expert, guiding the team on best practices in statistical analysis, experimentation design, and code quality.
- Partner with PMs and Engineers to translate ambiguous business questions into rigorous data science projects with actionable outcomes.
- Drive the adoption of MLOps best practices, ensuring models are scalable, reproducible, and monitored effectively in production.
- Perform deep-dive analyses on user behavior and product performance to identify opportunities for optimization and growth.
- Mentor and coach other data scientists and analysts, fostering a culture of continuous technical learning and innovation.
- Communicate complex technical findings to non-technical stakeholders through compelling data storytelling and visualization.
This role requires
- 7+ years of experience in DS, Statistics, or a related field, with a proven track record of delivering high-impact machine learning solutions.
- Expert-level proficiency in Python for data manipulation, statistical analysis, and model development (pandas, scikit-learn, numpy, etc.).
- Advanced knowledge of SQL for complex data querying and performance optimization.
- Strong hands-on experience with a breadth of modeling techniques, including:
- Supervised learning (Regression, Random Forests, Gradient Boosting like XGBoost/LightGBM)
- Unsupervised learning (Clustering, Dimensionality Reduction)
- Time series forecasting (ARIMA, Prophet)
- Experience with dbt for data transformation and maintaining clean, reliable data pipelines.
- Demonstrated ability to deploy models into production environments and familiarity with MLOps concepts.
- Familiarity with software engineering practices (CI/CD, version control with Git, Jira).
- Strong communication skills, with the ability to influence stakeholders and explain technical concepts to business audiences.
- Expertise in developing or integrating GenAI models or AI agents into product and analytical workflows.
Bonus points if you have
- Experience with cloud-based machine learning platforms, specifically AWS SageMaker.
- Background in the observability.
- Advanced degree (Master’s or PhD) in a quantitative discipline.
- Proficiency in data visualization tools (e.g., Tableau, Looker) to democratize insights.