Data Scientist

Job Description

About the Role:
We’re looking for a data scientist who thrives on turning product usage data into high-impact insights. You’ll work closely with Product, Engineering, and Growth teams to build predictive models, design experiments, and uncover behavioral trends that directly influence product decisions. This role is ideal for someone who has a strong statistical foundation, writes production-ready code, and thinks beyond dashboards.

Key Responsibilities:

• Build and maintain predictive models that forecast user retention, churn, and feature adoption with measurable accuracy.
• Design and analyze A/B and multivariate tests; advise on test power and business trade-offs.
• Work with behavioral event data (Clickstream, App Logs, Funnel Tracking) to uncover user drop-off points and recommend actions.
• Develop a framework for user segmentation based on behavioral clustering and persona modeling.
• Collaborate with engineers to deploy models and pipelines in production environments (not just notebooks).
• Partner with product managers to translate ambiguous questions into structured analysis, then communicate findings to non-technical stakeholders with clarity.
• Implement anomaly detection systems that alert product teams to changes in usage behavior or system drift.
• Review code and mentor junior data scientists in experimental design and statistical reasoning.

Required Qualifications:

• 4+ years of experience in a data science role with a focus on behavioral or product analytics.
• Advanced knowledge of SQL (window functions, CTEs, performance optimization) and Python (Pandas, NumPy, Scikit-learn).
• Demonstrated experience building and validating predictive models using techniques like logistic regression, random forests, gradient boosting, or time-series forecasting.
• Experience designing experiments with proper statistical power and handling messy real-world data (e.g., incomplete logs, backfilled events).
• Solid understanding of database architecture and ETL pipelines (experience with dbt or Airflow is a plus).
• Familiarity with event instrumentation frameworks like Segment, Amplitude, or Mixpanel.
• Proven ability to write clear documentation and present results tailored to both technical and non-technical audiences.

Nice to Have:

• Experience working on consumer-facing SaaS or mobile apps.
• Familiarity with real-time data processing frameworks (Kafka, Spark Streaming).
• Knowledge of causal inference techniques (e.g., propensity score matching, difference-in-differences).
• Prior contributions to open-source analytics tools or internal libraries for experimentation.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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