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