Enterprise Data Architect

Enterprise Data Architect

Key Responsibilities

• Lead end-to-end machine learning solution delivery for complex enterprise use cases
• Translate ambiguous business challenges into structured ML problem statements and solution architectures
• Design, develop, and optimise advanced machine learning models including:
• Supervised and unsupervised learning
• Ensemble methods
• Deep learning architecture
• Optimisation and probabilistic models
• Evaluate and select appropriate algorithms based on data characteristics, performance trade-offs, scalability, and interpretability requirements
• Apply knowledge of deep learning architectures such as:
• CNNs for vision use cases
• RNNs / LSTMs / GRUs for sequential data
• Transformer architectures for NLP and structured data
• Fine-tuning and transfer learning approaches
• Drive experimentation frameworks, hypothesis testing, model validation, and statistical rigor
• Ensure robustness, generalisation, bias mitigation, and explainability in deployed models
• Provide technical direction on feature engineering strategies and model performance enhancement
• Collaborate with engineering teams to transition models into scalable production systems
• Mentor data scientists and uphold modelling standards, documentation, and reproducibility best practices
• Contribute to reusable ML frameworks, accelerators, and innovation initiatives

Required Experience & Qualifications

• 15+ years of total professional experience , including
• 8+ years of hands-on experience in machine learning and data science
• Advanced degree (Master’s or PhD preferred) in Computer Science, Statistics, Mathematics, Engineering, or related quantitative discipline
• Proven experience building and deploying advanced ML and deep learning models in enterprise environments
• Deep understanding of algorithm selection, model complexity trade-offs, and overfitting/underfitting dynamics
• Strong proficiency in Python and ML ecosystems (scikit-learn, pandas, NumPy)
• Experience with deep learning frameworks (PyTorch or TensorFlow)
• Practical knowledge of deep learning architectures (CNNs, RNNs, Transformers) and when to apply them
• Strong SQL and data manipulation capabilities
• Experience working with large-scale datasets and distributed compute frameworks (e.g., Spark)
• Demonstrated ability to independently lead technical ML solution design
• Experience working in client-facing delivery environments
• Exposure to cloud-based ML platforms (AWS, Azure, or GCP)
• Experience in NLP, Computer Vision, time-series forecasting, or optimisation
• Experience with fine-tuning large language models or foundation models
• Familiarity with ML lifecycle management and monitoring practices