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