Responsibilities
- 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.
- 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.
- 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.
- 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.
- Familiarity to work with Git and GitHub.
- Dataform is a must
Nice to Have
- 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.
- Exposure to fast-scaling startup or tech environments is a strong plus
