Source description
About the role
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.
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