Head of Machine Learning & AI

Responsibilities:

1. ML Architecture, Strategy & Innovation

• Define and drive the ML strategy across business domains, identifying high-impact opportunities for automation, optimization, and prediction. 
• Architect scalable ML systems and reusable frameworks that support real-time inference, batch processing, and continuous learning. 
• Lead the evaluation and adoption of cutting-edge ML techniques (e.g., foundation models, causal inference, reinforcement learning) to solve complex business problems. 

1. End-to-End ML Lifecycle Ownership

• Lead the design, development, and deployment of advanced supervised and unsupervised models for use cases such as churn prediction, demand forecasting, fraud detection, and dynamic pricing. 
• Own the full ML lifecycle: from problem framing and data exploration to model training, validation, deployment, and monitoring. 
• Champion best practices in experimentation, reproducibility, and responsible AI. 

1. Cross-Functional Leadership & Business Impact

• Partner with senior stakeholders across Sales, Customer Service, Finance, Supply Chain, and Fulfillment to define and prioritize ML initiatives aligned with strategic goals. 
• Translate ambiguous business challenges into well-scoped ML solutions with measurable ROI. 
• Serve as a technical advisor to executive leadership on AI/ML trends, risks, and opportunities. 

1. MLOps, Governance & Infrastructure

• Lead the design and implementation of robust MLOps pipelines using tools like DataRobot 
• Ensure scalable, secure, and compliant deployment of models in cloud-native environments (AWS, Azure, GCP). 
• Establish governance frameworks for model versioning, monitoring, retraining, and auditability. 

1. Data Engineering & Feature Platform Design

• Collaborate with data engineering teams to define and evolve enterprise-wide feature stores, data contracts, and real-time data pipelines. 
• Drive innovation in feature engineering, leveraging domain knowledge and advanced statistical techniques. 

1. Mentorship, Collaboration & Thought Leadership

• Mentor junior ML engineers and data scientists, fostering a culture of technical excellence and continuous learning. 
• Contribute to internal knowledge sharing, technical design reviews, and ML community engagement. 
• Publish whitepapers, present at conferences, or lead internal workshops on emerging ML technologies. 

Qualifications:

Required:

• 8-10  years of experience in machine learning product management, AI engineering, or applied data science, with a strong foundation in software engineering. 
• Proven experience deploying and scaling ML models in production environments using modern MLOps practices. 
• Deep understanding of cloud ML platform capabilities (DataRobot is preferred)  
• Strong communication skills with the ability to influence technical and non-technical stakeholders. 

Preferred:

• Experience leading ML initiatives in enterprise domains such as Finance, Sales, or Supply Chain. 
• Familiarity with advanced ML techniques (e.g., transformers, graph neural networks, time series forecasting, causal modeling). 
• Exposure to enterprise platforms such as Salesforce, Oracle.  
• Graduate degree (MS or PhD) in Computer Science, Machine Learning, Statistics, or a related field. 

  Role & responsibilities