Data Scientist

Overview

The role focuses on building and scaling data-driven solutions that bridge the gap between complex statistical modeling and business-critical decision making. This position involves translating high-level business objectives into rigorous technical experiments, ensuring that every model developed is grounded in sound methodology and optimized for real-world performance.

The team operates at the intersection of product and engineering, meaning the role requires a deep understanding of experimental design, causal inference, and predictive modeling. This is an opportunity to own the full analytical lifecycle, from initial data discovery and feature engineering to post-deployment validation and iterative improvement.

• Develop and deploy predictive models using Python, scikit-learn, and XGBoost to solve core business problems including churn prediction and lifetime value forecasting
• Design and execute sophisticated A/B and multivariate tests, utilizing frequentist and Bayesian methods to quantify product impact
• Build automated data pipelines in SQL and dbt to transform raw telemetry data into clean, model-ready features
• Collaborate with engineering teams to integrate machine learning models into production services via REST APIs and microservices
• Create and maintain interactive dashboards in Looker or Tableau to communicate model performance and experimental results to non-technical stakeholders
• Perform deep-dive exploratory data analysis (EDA) to identify emerging trends and provide actionable recommendations for product roadmaps

• 3–6 years of experience in a Data Science or Applied Research role, preferably within a high-growth technology environment
• Advanced proficiency in Python and SQL, with a proven track record of writing production-quality code and complex analytical queries
• Strong foundation in statistical theory, including hypothesis testing, probability distributions, and longitudinal data analysis
• Hands-on experience with modern data stacks, specifically Snowflake, Big Query, or Redshift, and version control using Git
• Master’s or PhD in a quantitative field such as Statistics, Computer Science, Economics, or Mathematics
• Bonus:
  Experience with PySpark for large-scale data processing or working with LLM evaluation frameworks for text-based features