Senior Data Scientist

Responsibilities:

• Lead end-to-end machine learning projects, from data exploration, modelling, and deployment, ensuring alignment with business objectives.
• Utilise traditional AI/data science methods (e. g., regression, classification, clustering) and advanced AI methods (e. g., neural networks, NLP) to address business problems and optimise processes.
• Implement and experiment with Generative AI models based on business needs using Prompt Engineering, Retrieval Augmented Generation (RAG) or Finetuning, using LLM's, LVMs, TTS, etc.
• Collaborate with teams across Digital & Innovation, business stakeholders, software engineers, and product teams, to rapidly prototype and iterate on new models and solutions.
• Mentor and coach junior data scientists and analysts, fostering an environment of continuous learning and collaboration.
• Adapt quickly to new AI advancements and technologies, continuously learning and applying emerging methodologies to solve complex problems.
• Work closely with other teams (e. g., Cybersecurity, Cloud Engineering) to ensure the successful integration of models into production systems.
• Ensure models meet rigorous performance, accuracy, and efficiency standards, performing cross-validation, tuning, and statistical checks.
• Communicate results and insights effectively to both technical and non-technical stakeholders, delivering clear recommendations for business impact.
• Ensure adherence to data privacy, security policies, and governance standards across all data science initiatives.

Requirements:

• Proficient in Python, R, SQL for data analysis, modelling, and data pipeline development, and JavaScript.
• MLOps experience.
• Experience with DevSecOps practices, and tools such as GitHub, Azure DevOps, Terraform, Bicep, AquaSec, Containerization tools (understanding), etc.
• Experience with cloud platforms (Azure, AWS, Google Cloud) and large-scale data processing tools (e. g., Hadoop, Spark).
• Strong understanding of both supervised and unsupervised learning models and techniques.
• Experience with frameworks like TensorFlow, PyTorch, and working knowledge of Generative AI models like GPT and GANs.
• Hands-on experience with Generative AI techniques, but with a balanced approach to leveraging them where they can add value.
• Proven experience in rapid prototyping and ability to iterate quickly to meet business needs in a dynamic environment.

Educational Requirements:

• Bachelor's degree in Data Science, Machine Learning, Computer Science, Statistics, or a related field. A master's degree or a PhD is a plus.
• 7+ years of experience in data science, machine learning, or AI, with demonstrated success in building models that drive business outcomes.