Data Scientist

About The Role

The role focuses on building, validating, and scaling statistical models and machine learning algorithms to drive strategic decisions and product features. The team works on high-impact problems ranging from predictive customer behavior modeling to algorithmic optimization of internal operations.

The role requires bridging the gap between raw data and actionable intelligence, collaborating with engineers to productionize models and translating complex mathematical concepts into clear business strategies for cross-functional stakeholders.

Key Responsibilities

• Develop, evaluate, and deploy predictive models using Python, R, and SQL to address critical business challenges and optimize performance metrics.
• Design and execute rigorous A/B tests, multivariate experiments, and causal inference studies to measure the impact of new product features.
• Build scalable, automated feature engineering and data preprocessing pipelines in collaboration with data platform and engineering teams.
• Conduct deep-dive exploratory data analysis (EDA) on high-dimensional datasets to discover patterns, anomalies, and potential optimization vectors.
• Establish model monitoring pipelines to track performance degradation, data drift, and prediction latency in production systems.
• Present statistical findings and strategic recommendations to executive leadership through clear visualizations and structured communication.
  
  

What We Are Looking For

• 3–6 years of experience as a Data Scientist, with a proven track record of deploying statistical and ML models into production environments.
• Strong proficiency in Python (including pandas, numpy, scikit-learn) and advanced SQL for processing and analyzing large-scale datasets.
• Solid theoretical foundation in statistics, probability, hypothesis testing, experimental design, and regression/classification techniques.
• Experience with distributed computing frameworks like PySpark or Databricks, and cloud data warehouses such as Snowflake or BigQuery.
• Master’s or PhD in Statistics, Computer Science, Applied Mathematics, Economics, or a related quantitative field.
• Bonus: Experience with deep learning frameworks (PyTorch, TensorFlow) or LLM API integrations for unstructured text analysis.