Develop, and deploy end-to-end machine learning models for complex business problems across forecasting, optimization, and prediction domains
Build and fine-tune large language models (LLMs) for enterprise applications including document intelligence, conversational AI, and decision support systems
Substantial understanding of solving data science and AI enabled problems in supply chain, finance, commercial or operations domain or AI agents with reasoning capabilities using LLMs
Adapt to a wide range of technical challenges across technologies to design a solution applicable to the business issue
Translate business requirements into technical AI/ML features, model selection, architecture decisions
Conduct exploratory data analysis and communicate insights
Collaborate with data engineers, architects, and business analysts on integrated solutions
Build feature engineering pipelines and automated data preparation workflows
Design AI solutions for commercial transformation including pricing optimisation, customer segmentation, and revenue management
Deploy and manage agentic AI and automation solutions, ensuring continuous learning, adaptation, and improvement in live business environments.
Contribute to proposals and technical assessments for new opportunities and internal knowledge transfer
Essential Qualifications
Degree or equivalent certification in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field
Essential Criteria
Experience designing and delivering solutions using large language models (e.g., GPT, Claude, Llama, Mistral or similar)
Practical exposure to at least one modern AI/ML platform such as Databricks (MLflow, AutoML), Azure Machine Learning, or Snowflake (Snowpark ML, Cortex)
Understanding of natural language processing, computer vision, and recommender systems
Strong programming skills in Python, SQL and proficiency with ML libraries (e.g. scikit-learn, pandas, NumPy, XGBoost, LightGBM)
Soft skills
Strong analytical and problem-solving mindset with attention to detail
Ability to work independently and drive projects from ambiguous requirements
Story telling with data and insights from the outputs
Consulting skills, supporting development of presentation decks and communication
Experience or knowledge covering at least of the following areas:
Good knowledge of key machine learning techniques, including supervised, unsupervised, and reinforcement learning
Experience applying statistical modelling, time-series forecasting, and predictive analytics in practical scenarios
Experience with deep learning frameworks (e.g. TensorFlow, PyTorch, Keras)
Knowledge of prompt engineering, RAG (Retrieval Augmented Generation), and LLM fine-tuning techniques
Familiarity with distributed computing frameworks (e.g. Spark)
Knowledge of graph neural networks, reinforcement learning, or causal inference
Experience with AI governance, model risk management, and regulatory compliance
Experience using Pro code and Low code tools such as LangGraph, UiPath, AutoGen, Semantic Kernal, and MS CoPilot and Power Platform