Sr. Data Scientist (AI & ML)

Role Overview

Machine Learning Delivery

• Frame ambiguous business problems as well-defined machine learning and data science problems with objective success criteria.
• Design, build, and ship production machine learning and generative AI systems spanning data pipelines, feature engineering, model training, serving, and monitoring.
• Architect robust ML systems, write clean and scalable production code, and maintain reliable models that solve business problems at scale.
• Train, evaluate, and iterate on models using appropriate algorithms and architectures to deliver measurable business value.

Generative AI and Data

• Use large language models and generative AI for data enrichment, smart content understanding, and automated decision-making.
• Build and maintain data models, features, and pipelines that support model training and performance measurement.
• Develop familiarity with source data and its generating systems through documentation, engineering collaboration, and systematic data profiling.

Experiments and Insights

• Provide high-quality insights and data-driven recommendations through rigorous analysis and automated reporting.
• Design, plan, and analyze A/B and multivariate experiments to measure model and product impact.
• Partner with product and business teams to identify high-impact opportunities and translate them into ML solutions and actionable recommendations.

Team and Practice Development

• Mentor other data scientists in their professional growth.
• Improve engineering and ML best practices, tooling, MLOps, internal ways of working, and training programs.

Requirements

• Ability to frame ambiguous business problems as well-defined machine learning and data science problems with objective success criteria.
• Ability to design, build, and ship end-to-end machine learning and generative AI systems in production.
• Experience with data pipelines, feature engineering, model training, serving, and production monitoring.
• Ability to write clean, scalable production code and maintain reliable ML models.
• Ability to design and analyze A/B and multivariate experiments to measure model and product impact.
• Ability to partner with product and business teams to translate opportunities into ML solutions and data-driven recommendations.
• Ability to mentor other data scientists and improve engineering, ML, tooling, MLOps, and training practices.