Senior Data Scientist

Role & Responsibilities:

• Model Development and Optimization:
• Lead the design, development, and deployment of advanced ML models for complex use cases, such as recommendation systems, fraud detection, customer segmentation, and demand forecasting.
• Partner with data engineers and product teams to ensure models are scalable, reliable, and aligned with business needs.
• Continuously optimize algorithms for performance, accuracy, and efficiency.
• Deployment and Integration:
• Own end-to-end model deployment processes into production environments using Kubernetes and cloud platforms (AWS, GCP).
• Define and manage MLOps best practices, including model monitoring, automated retraining, and CI/CD for ML pipelines.
• Champion automation of model training, validation, and deployment workflows to improve system reliability.
• Analytics Strategy & Enablement:
• Act as a custodian of organizational data, ensuring data quality, consistency, and readiness for advanced analytics and modeling.
• Translate complex data insights into business impact, clearly communicating ROI to stakeholders.
• Drive adoption of analytics and data-driven decision-making across teams by mentoring and enabling business stakeholders.
• Leadership & Collaboration:
• Mentor junior data scientists and analysts, providing technical guidance and career development support.
• Collaborate closely with engineering and product leadership to shape the company’s data science strategy.
• Stay ahead of emerging AI/ML trends, tools, and research, and advocate for their adoption when relevant.

Requirements:

• Proficiency in Python and SQL, with hands-on expertise in ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Strong knowledge of deployment tools (Docker, Kubernetes, cloud platforms) and MLOps best practices.
• Proven ability to design and maintain production-grade ML systems.
• Deep understanding of statistical analysis, hypothesis testing, and data visualization.
• Knowledge of cloud-serverless technologies (AWS Lambda, GCP Functions, Azure Functions).
• Strong familiarity with GCP is a plus.
• Prior experience deploying ML solutions in E-commerce or high-growth environments is highly desirable.
• Familiarity to work with Git and GitHub.
• Dataform is a must

Experience:

• 5+ years in data science, including hands-on ML model development and deployment.
• 3+ years in data analytics, statistical modeling, and experimentation.
• Experience mentoring or leading junior data scientists.
• Exposure to fast-scaling startup or tech environments is a strong plus