The Data Scientist will design, develop, and deploy machine learning and AI solutions that solve real business challenges across banking, healthcare, and other industries.

You will work across the full AI lifecycle, from data exploration and feature engineering to predictive modelling, machine learning, Generative AI, and MLOps. Working closely with Software Engineers, Technical Leads, and Solution Architects, you will help transform data into intelligent solutions that deliver measurable business value.

• Design, develop, train, and evaluate machine learning models for predictive analytics and business intelligence.
• Build and optimise classification, regression, clustering, recommendation, forecasting, anomaly detection, and other predictive models.
• Analyse structured and unstructured datasets to uncover business insights and opportunities.
• Prepare, clean, transform, and engineer datasets for machine learning applications.
• Develop, validate, and monitor machine learning models throughout their lifecycle.
• Build and optimise Retrieval-Augmented Generation (RAG) pipelines and Generative AI solutions where applicable.
• Evaluate Large Language Models (LLMs) and improve AI solution performance through prompt engineering, retrieval optimisation, and model evaluation.
• Develop AI and machine learning pipelines using enterprise AI platforms.
• Work closely with Software Engineers to deploy AI models into production applications.
• Research emerging AI and machine learning technologies and recommend practical solutions for customer use cases.
• Document methodologies, experiments, and technical findings to support knowledge sharing and continuous improvement.

Required Qualifications & Experience

• Bachelor's Degree in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related discipline.
• Minimum 3 years of experience in Data Science, Machine Learning, or Artificial Intelligence.
• Strong proficiency in Python for data science and machine learning.
• Solid understanding of supervised and unsupervised machine learning algorithms.
• Experience building predictive models using real-world datasets.
• Experience with feature engineering, model training, validation, and optimisation.
• Strong knowledge of statistics, probability, and data analysis techniques.
• Experience with SQL and data querying.
• Experience using Git and collaborative development workflows.
• Strong analytical thinking and problem-solving skills.
• Excellent communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.

Preferred Qualifications

Experience in one or more of the following areas is highly desirable.

Machine Learning & Data Science

• Predictive Analytics
• Classification, Regression, Clustering, Forecasting, Recommendation Systems
• Time Series Analysis
• Feature Engineering
• Model Evaluation and Optimisation
• Statistical Modelling
• Natural Language Processing (NLP)
• Computer Vision
• Explainable AI (XAI)

AI & Generative AI

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• AI Agents and Agentic Workflows
• Prompt Engineering
• AI Evaluation Frameworks
• Embeddings and Semantic Search
• Vector Databases

AI & Data Platforms

• Databricks
• Dataiku
• Snowflake
• Microsoft Fabric
• Azure Machine Learning
• AWS SageMaker
• MLflow
• MLOps pipelines
• Model deployment and monitoring

Programming & Cloud

• Scikit-learn
• TensorFlow or PyTorch
• Pandas and NumPy
• Docker
• Azure or AWS
• CI/CD for machine learning solutions