Description

Key Responsibilities

Machine Learning Strategy & Development

• Lead development of machine learning solutions supporting:
  • Recommendation engines
  • Customer segmentation
  • Churn prediction
  • Customer value prediction
  • Offer optimization
  • Content personalization
• Design, train, evaluate, and deploy predictive models.
• Create reusable ML frameworks and model development standards.

Customer Intelligence & Personalization

• Leverage Customer 360 and Identity Graph capabilities to improve prediction accuracy.
• Develop intelligent customer insights and personalization capabilities.
• Build models that improve customer engagement, retention, and loyalty outcomes.
• Collaborate with business leaders to align machine learning priorities with strategic objectives.

AI & Next-Generation Capabilities

• Support development of domain-specific AI agents.
• Partner with engineering teams on AI-powered business experiences.
• Contribute to LLM-enabled solutions and intelligent assistant initiatives.
• Evaluate emerging AI capabilities and opportunities for enterprise adoption.

Production ML & Scaling

• Work closely with MLOps Engineers to operationalize models.
• Develop scalable real-time inference solutions.
• Build monitoring, model validation, and retraining processes.
• Optimize model performance and reliability in production.

Technical Leadership

• Provide technical leadership across Data Science, Engineering, and MLOps teams.
• Mentor ML Engineers and Data Scientists.
• Lead architecture discussions and model review sessions.
• Establish best practices for enterprise AI delivery.

Required Qualifications

• 8+ years of Machine Learning Engineering or Applied AI experience.
• 3+ years in a Lead, Principal, or Senior Technical Leadership role.
• Proven experience delivering production-grade machine learning systems.
• Strong expertise with:
  • Databricks
  • MLflow
  • Python
  • Machine Learning Frameworks (TensorFlow, PyTorch, Scikit-learn)
  • Real-time inference architectures
• Experience building:
  • Recommendation systems
  • Personalization engines
  • Predictive analytics solutions
  • Customer intelligence platforms
• Deep understanding of feature engineering and ML lifecycle management.
• Experience partnering with Data Engineering teams to design ML-ready datasets.

Preferred Qualifications

• Customer 360 implementation experience.
• Identity Graph experience.
• Hospitality, Gaming, Entertainment, Retail, Loyalty, or Consumer Digital experience.
• Experience with:
  • Snowflake
  • MLOps practices
  • GenAI solutions
  • Agentic AI architectures
  • LLM-powered applications
  • Real-time recommendation platforms
• Experience supporting enterprise AI transformation initiatives

Key Skills

Education

Any Gradute