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