Data Scientist

About The Role

The role drives the development, validation, and deployment of predictive models that optimize core business processes. Operating within a highly collaborative team of data engineers and product managers, this position translates complex business requirements into robust statistical frameworks and machine learning algorithms. This position is critical for scaling data-driven decision-making across the organization, focusing on improving predictive accuracy, ensuring model explainability, and maintaining high availability in production pipelines.

Key Responsibilities

• Develop, evaluate, and deploy predictive and prescriptive models using Python and R, specifically targeting classification, regression, and time-series forecasting challenges
• Design and optimize complex SQL queries and PySpark pipelines to extract, transform, and aggregate structured and unstructured data from multi-terabyte data warehouses
• Establish rigorous model validation protocols, statistical testing methodologies, and A/B testing frameworks to measure business impact and model performance
• Collaborate with MLOps and engineering teams to containerize models using Docker and deploy them as microservices on AWS or Kubernetes platforms
• Maintain models in production by monitoring for data drift, concept drift, and performance degradation, implementing automated retraining loops where necessary
• Communicate complex modeling results, methodology trade-offs, and statistical findings to non-technical stakeholders through interactive dashboards and clear presentations

What We Are Looking For

• 3-6 years of professional experience as a Data Scientist or Applied Statistician, with a proven track record of deploying models to production environments
• Strong proficiency in Python (including pandas, NumPy, scikit-learn, and XGBoost) and advanced SQL for complex data manipulation
• Solid theoretical foundation in statistics, probability, experimental design, and machine learning algorithms
• Experience with cloud data warehouses (such as Snowflake, BigQuery, or Redshift) and workflow orchestration tools like Apache Airflow
• Master's or Ph.D. in Computer Science, Statistics, Applied Mathematics, Economics, or a related quantitative field
• Bonus: Experience with deep learning frameworks (PyTorch, TensorFlow) or implementing natural language processing (NLP) pipelines in production