Data Scientist

About The Role

This role focuses on bridging the gap between raw data and actionable business intelligence by designing, building, and scaling machine learning models that drive core product decisions. The team works at the intersection of statistical modeling and scalable engineering, ensuring that every model developed can withstand the rigors of a high-traffic production environment.

The role involves collaborating with product managers and engineers to identify high-impact opportunities for predictive modeling, ranging from customer lifetime value forecasting to real-time recommendation engines. Success is measured by the ability to move beyond experimental notebooks into robust, automated pipelines that deliver measurable ROI.

Key Responsibilities

• Develop and deploy end-to-end machine learning pipelines using Python, SQL, and orchestration tools like Airflow or Prefect
• Perform deep-dive exploratory data analysis on multi-terabyte datasets to identify features, trends, and anomalies that inform model architecture
• Implement and optimize statistical models using frameworks such as XGBoost, LightGBM, and scikit-learn for both batch and real-time inference
• Build automated model monitoring and validation frameworks to track performance metrics, data drift, and prediction accuracy over time
• Collaborate with backend engineering teams to integrate model outputs into customer-facing applications via REST APIs and microservices
• Design and execute rigorous A/B tests and multivariate experiments to quantify the impact of model iterations on business KPIs

What We Are Looking For

• 3–6 years of professional experience in data science or machine learning roles, with a proven track record of shipping models to production
• Advanced proficiency in Python and SQL, including experience with data processing libraries like Pandas, NumPy, and Dask
• Strong foundation in statistical theory, including hypothesis testing, experimental design, and Bayesian inference
• Hands-on experience with cloud-native data environments such as Snowflake, BigQuery, or Databricks
• MS or PhD in a quantitative field such as Computer Science, Statistics, Mathematics, or Physics
• Bonus: Experience with Deep Learning frameworks (PyTorch/TensorFlow) or MLOps tools like MLflow and Kubeflow