Enterprise Data Science Tech Lead Vodafone

Job Description

Roles & Responsibilities

Desired Candidate Profile

Solution Implementation

• Translate customers requirements into actionable data science tasks and implementation plans.
• Lead the development, training, and deployment of AI models and data pipelines tailored to customer-specific requirements.
• Ensure models and AI components are scalable, efficient, and production-ready, with smooth integration into customer systems and enterprise platforms.

Technical Leadership

• Serve as the subject matter expert (SME) in machine learning, data science, and AI engineering practices.

Provide hands-on technical guidance to data scientists, engineers, and MLOps

• teams, ensuring accurate and effective implementation.
• Lead technical squads through virtual collaboration, guaranteeing solutions are built to meet both customer expectations and technical standards.

Decision Execution

• Validate model performance, data quality, and technical fit before solution handover.
• Collaborate with the Enterprise Tech Leads and Product Owners to review and confirm solution readiness based on business impact, feasibility, and model validation results.
• Provide input on task prioritization based on complexity, risk, and technical dependencies.

Customer Support

• Work alongside the Enterprise team to ensure implemented AI solutions meet business outcomes and address real customer needs.
• Explain data science approaches, results, and business insights in a clear and accessible manner to both technical and non-technical stakeholders.
• Contribute to building customer confidence in Vodafone s AI capabilities by ensuring reliable and transparent solution delivery.

Value Realization

• Identify technical opportunities to improve performance, accuracy, and efficiency of existing AI solutions.
• Recommend data science enhancements and automation strategies that can further strengthen the customer s business outcomes.
• Contribute to refining reusable assets, components, and best practices for enterprise AI delivery

Customer-Focused Execution

• Strong communication skills to understand solution requirements in collaboration with Enterprise Tech leads and Product Owners and translate them into data science tasks.
• Ability to present data-driven insights, model performance, and implementation results in a clear, business-relevant manner.

Problem Solving and Innovation

• Strong analytical thinking and debugging skills to address real-world AI deployment challenges (e.g., model drift, data quality, latency).

Creativity in designing tailored AI pipelines that are both effective and maintainable in production

Collaboration and Delivery Leadership

• Proven ability to collaborate with engineers, data scientists, MLOps specialists, and architects to deliver high-quality AI solutions.
• Leadership in execution: mentoring technical team members, performing code reviews, and ensuring delivery excellence
• p> Must-Have Technical / Professional Qualifications
  • Bachelor s or master s degree in computer science, Data Science, Artificial Intelligence, or a related field.
  • 5 7 years of hands-on experience in developing and deploying AI/ML models in production environments.
  • Advanced proficiency in ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
  • Strong expertise in building advanced Generative AI (GenAI) and Natural Language Processing (NLP) applications using modern large language models and frameworks (e.g., Hugging Face, Langchain, OpenAI APIs, Agents Frameworks).
  • Experience with big data processing and distributed computing using Apache Spark or similar technologies.
  • Proficiency in building, deploying, and managing AI, GenAI and big data applications on cloud platforms (e.g., AWS, Azure, GCP).
  Experience with MLOps tools and techniques for versioning, CI/CD, monitoring, and scaling models