Lead Data Scientist – AI, Machine Learning

Lead Data Scientist – AI, Machine Learning & Advanced Analytics

Description of the Role

• Design, develop, and implement advanced data science, artificial intelligence, and machine learning solutions to address complex business challenges and improve organizational performance.
  • Apply statistical, mathematical, analytical, and machine learning techniques to extract insights from structured and unstructured data and support data-driven decision-making.
  • Collaborate with business stakeholders, data engineers, and technology teams to translate business requirements into scalable AI, machine learning, and analytics solutions across cloud and on-premises environments.

Core Responsibilities

• Collect, prepare, cleanse, validate, and analyze large and complex datasets to identify trends, patterns, anomalies, and opportunities for business improvement.
  • Design, develop, test, evaluate, and optimize machine learning, predictive analytics, and artificial intelligence models using statistical and data science methodologies.
  • Build and implement Retrieval-Augmented Generation (RAG) solutions and generative AI applications leveraging Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, and related AI platforms.
  • Develop, train, evaluate, and maintain custom machine learning and computer vision models using Azure-native AI services, YOLO frameworks, and on-premises machine learning infrastructure.
  • Establish and govern MLOps practices, including model lifecycle management, automated training and deployment pipelines, model monitoring, performance evaluation, and continuous improvement.
  • Analyze business problems and translate them into data science, analytics, and AI opportunities that deliver measurable business outcomes and operational efficiencies.
  • Collaborate with data engineering, infrastructure, and architecture teams to define data requirements, solution architecture, and deployment strategies for enterprise AI and analytics solutions.
  • Interpret analytical findings and model outputs, communicate insights to stakeholders, and provide evidence-based recommendations to support strategic and operational decision-making.