Role & Responsibilities :

- Translate ambiguous business and product problems into well-defined data science and machine learning problems, with clear success metrics and evaluation criteria.

- Apply statistical analysis and experimentation techniques to support data-driven decision-making, including hypothesis testing, A/B testing, and causal analysis.

- Develop, evaluate, and improve machine learning models using appropriate algorithms, metrics, validation techniques, and feature engineering approaches.

- Perform complex SQL-based data analysis, investigate data quality issues, and work with structured data models to derive meaningful insights.

- Build reproducible and production-quality data science workflows using Python/R, Git, testing, documentation, and appropriate development practices.

- Own projects end-to-end, from data discovery and problem definition through modeling, validation, deployment planning, and monitoring.

- Partner with business, product, engineering, and other stakeholders to understand requirements, communicate findings, and influence data-driven decisions.

- Identify risks related to data availability, model performance, privacy, bias, and feedback loops, and proactively develop mitigation strategies.

- Communicate complex analytical results and model outputs in a clear and actionable manner to both technical and non-technical stakeholders.

- Contribute to improving data science practices, reusable frameworks, model quality, and analytical standards across the team.

Preferred Candidate Profile :

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.

- Strong hands-on experience in Python or R, with a solid understanding of data analysis and machine learning workflows.

- Strong knowledge of statistics, experimentation, hypothesis testing, A/B testing, and causal inference.

- Strong proficiency in SQL, including complex joins, CTEs, window functions, and data validation.

- Practical experience developing and evaluating supervised machine learning models, including regression, tree-based models, boosting, and regularization.

- Good understanding of model evaluation, feature engineering, cross-validation, data leakage, imbalanced datasets, and model interpretability.

- Experience taking machine learning/data science projects from problem definition to implementation and business delivery.

- Strong analytical and problem-solving ability, with a product-oriented and business-focused mindset.

- Excellent communication and stakeholder management skills, with the ability to explain technical concepts and business impact clearly.

- Experience with Git and reproducible development practices.

Good to Have :

- Experience with MLOps, model deployment, monitoring, CI/CD, Docker, or Kubernetes.

- Exposure to AWS, Azure, or GCP cloud-based ML platforms.

- Experience with PyTorch/TensorFlow, NLP, Computer Vision, recommender systems, or transformers.

- Knowledge of Spark, Databricks, Airflow, Kafka, or other data engineering technologies.

- Experience with advanced causal inference, optimization, forecasting, or reinforcement learning.

- Relevant industry/domain experience such as fintech, healthcare, supply chain, pricing, fraud/risk, or similar areas.

- Experience mentoring team members or contributing to data science best practices and reusable frameworks.