GEN AI Architect

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.