D&T Lead - Data Science

Purpose of the Job The Lead – Data Science is a senior hands-on role within Data & Analytics, responsible for designing, building, deploying, and operationalizing end-to-end AI/ML and Generative/Agentic AI solutions that drive business value. The role leads initiatives from experimentation to scalable, production-grade deployment on GCP (Vertex AI). Key purpose: Deliver end-to-end AI/ML and GenAI/Agentic AI solutions across the MLOps lifecycle. Build scalable training, serving, and monitoring pipelines on GCP/Vertex AI. Apply Agentic AI frameworks and tokenomics best practices to optimize performance, reliability, and cost Partner with business and engineering teams to translate complex problems into production-ready data science solutions, particularly in supply chain and logistics. Job Description AI/ML & GenAI Solution Delivery Design, develop, and deploy end-to-end AI/ML and GenAI/Agentic AI solutions, owning the complete lifecycle from data preparation and model development to deployment and monitoring. Build and optimize Agentic AI workflows using modern frameworks, applying best practices for agent orchestration, tool use, and multi-step reasoning. Apply tokenomics expertise to optimize prompt design, context management, and model selection for cost efficiency and performance at scale. MLOps & Engineering Establish and maintain robust MLOps practices, including automated model training, versioning, deployment, and continuous monitoring. Build and manage CI/CD pipelines for ML models and AI applications to enable reliable, repeatable, and automated releases. Develop and orchestrate data and ML workflows using Apache Airflow (DAGs) to ensure timely, dependable pipeline execution. Cloud & Platform Build and operate solutions on GCP, leveraging Vertex AI for model training, tuning, deployment, and serving. Utilize GCP services such as AlloyDB, Cloud Run, Cloud Batch, and Cloud SQL to build scalable, performant, and cost-effective solutions. Implement monitoring solutions to track model health, performance, drift, and resource utilization in production. Collaboration & Ownership Work closely with data engineers, product teams, and business stakeholders to understand requirements and deliver fit-for-purpose AI/ML solutions. Take end-to-end ownership of assigned initiatives, enhancements, and issues through to closure. Participate in design and architecture discussions, providing practical, solution-oriented recommendations. Continuous Improvement Contribute to improving data science and MLOps standards, best practices, and documentation. Stay current with advances in GenAI, Agentic AI, and the broader ML tooling ecosystem. Support knowledge sharing and mentor team members on AI/ML best practices. Job Requirements - Experience and Education Core Technical Skills 3–4 years of hands-on experience in AI/ML, with demonstrable experience in GenAI and Agentic AI. Bachelor's degree in Computer Science, Data Science, Engineering, or a related field (Master's preferred). Proven ability to build end-to-end AI/ML solutions, including full MLOps implementation (training, deployment, monitoring, retraining). Hands-on experience building and managing CI/CD pipelines for ML/AI workloads. Working experience with Vertex AI for model development, training, and serving. Strong working experience on Google Cloud Platform (GCP). Expertise in tokenomics and best practices for Agentic AI frameworks. Familiarity with GCP services such as AlloyDB, Cloud Run, Cloud Batch, and Cloud SQL. Hands-on experience building and orchestrating workflows with Apache Airflow (DAGs). Strong proficiency in Python for model development, data processing, and automation. Strong analytical and troubleshooting skills with the ability to think quickly under pressure. Ability to translate ambiguous business problems into workable technical solutions. Proven track record of driving initiatives to completion with minimal supervision. Clear communication skills, with the ability to explain complex AI/ML concepts in a simple, practical manner. Leadership Behaviors Building Outstanding Teams Setting a clear direction Simplification Collaborate & break silos Execution & Accountability Growth mindset Innovation Inclusion External focus Skills Adaptability Resilience Attention To Details Communication Skills Cross-Functional Collaboration Change Management Problem Solving Analytical Thinking Collaborative Mindset Problem Diagnosis and Resolution