Data Scientist

About The Role

The role focuses on building, scaling, and deploying statistical models and machine learning algorithms to drive core product decisions and user engagement. The team acts as the bridge between raw data streams and productionized intelligent features, transforming complex datasets into actionable predictive power. This position requires a practitioner who thrives on autonomy, possesses deep statistical intuition, and is passionate about deploying models that directly impact business metrics. The team operates in a fast-paced environment where rigorous experimentation and rapid iteration are highly valued.

Key Responsibilities

• Develop, evaluate, and productionize predictive models and segmentation algorithms using Python, pandas, and scikit-learn
• Design and execute rigorous A/B testing frameworks, power analyses, and multivariate experiments to validate model performance and product features
• Build robust data pipelines and feature engineering workflows in SQL and PySpark to support model training and offline evaluation
• Collaborate with product and engineering teams to integrate machine learning models into backend microservices and user-facing applications
• Establish automated model monitoring systems to track performance drift, data quality anomalies, and inference latency in production
• Translate complex analytical findings into clear, structured insights and present recommendations to cross-functional stakeholders

What We Are Looking For

• 3–6 years of professional experience as a Data Scientist or Quantitative Analyst, with a proven track record of deploying models to production environments
• Strong proficiency in Python and SQL, including experience with data science libraries such as NumPy, pandas, scikit-learn, and XGBoost
• Deep understanding of statistical methodology, including hypothesis testing, regression analysis, experimental design, and causal inference
• Experience working with cloud data warehouses (e.g., Snowflake, BigQuery) and distributed computing frameworks like Spark
• BS/MS or PhD in a quantitative field such as Computer Science, Statistics, Applied Mathematics, Economics, or Physics
• Bonus: Experience with deep learning frameworks (PyTorch, TensorFlow), MLflow for model tracking, or deploying models via Docker and Kubernetes