Data Scientist

Key Responsibilities:
- Define, design, and execute end-to-end data science projects including problem framing, data collection, exploratory analysis, feature engineering, model development, validation, and deployment
- Build, train, evaluate, and iterate on machine learning and statistical models including regression, classification, clustering, time series forecasting, and deep learning architectures
- Perform in-depth exploratory data analysis on large and complex datasets to uncover patterns, trends, anomalies, and actionable business insights
- Collaborate with business stakeholders, product managers, and engineering teams to translate business problems into well-defined data science problems and deliver scalable solutions
- Design and execute A/B tests, multivariate experiments, and causal inference studies to validate hypotheses and measure the impact of product and business decisions
- Build and maintain data pipelines, feature stores, and model serving infrastructure in collaboration with data engineering and MLOps teams
- Develop clear and compelling data visualizations, dashboards, and executive presentations to communicate findings and model outcomes to technical and non-technical audiences
- Implement MLOps best practices including model versioning, experiment tracking, monitoring, and retraining pipelines to ensure models remain accurate and reliable in production
- Stay updated with latest research in machine learning, deep learning, NLP, and AI and evaluate applicability of new techniques to existing business problems
- Mentor junior data scientists and analysts and contribute to building a strong data science culture within the team