Data Scientist

About The Role

The role drives the development of advanced statistical models and predictive algorithms to solve complex business and product problems. This position sits at the intersection of data engineering, product strategy, and machine learning, translating raw telemetry and behavioral data into actionable production models.

The team focuses on high-impact initiatives where predictive accuracy and experimental rigor directly influence product decisions. You will collaborate with product managers, data engineers, and software architects to scale automated decisioning systems and establish robust experimentation frameworks.

Key Responsibilities

• Design, prototype, and scale machine learning models and statistical analyses using Python, SQL, and PySpark to optimize product features and user engagement
• Establish rigorous experimentation frameworks, including A/B testing methodologies, multi-armed bandits, and quasi-experimental designs for features where standard randomized trials are difficult
• Develop and maintain feature engineering pipelines, ensuring consistency between offline training datasets and online production feature stores
• Build interactive dashboards and automated reporting systems in Tableau or Looker to communicate model performance and insights to executive stakeholders
• Collaborate with MLOps and data engineering teams to transition prototype models from notebooks to production APIs using CI/CD pipelines and containerized environments
• Conduct deep-dive statistical analyses on user behavior datasets to uncover latent trends, validate hypotheses, and inform the long-term product roadmap

What We Are Looking For

• 3–6 years of professional experience as a Data Scientist or Quantitative Analyst, with a track record of deploying predictive models into production environments
• Expert-level proficiency in Python and standard data science libraries, including pandas, scikit-learn, NumPy, and Statsmodels
• Advanced SQL skills, with experience querying massive, complex datasets in Snowflake, BigQuery, or Redshift
• Strong foundation in applied statistics: hypothesis testing, regression analysis, causal inference, and experimental design
• Master's or Ph.D. in a quantitative field (Statistics, Computer Science, Economics, Mathematics, or a related discipline)
• Bonus: Experience with deep learning frameworks (PyTorch, TensorFlow), MLflow for experiment tracking, or building models in AWS/GCP cloud environments