Data scientist- Principal Data Scientist/Senior Machine Learning Scientist/AI/ML Solution ArchitectKey ResponsibilitiesLead end-to-end machine learning solution delivery for complex enterprise use casesTranslate ambiguous business challenges into structured ML problem statements and solution architecturesDesign, develop, and optimise advanced machine learning models including:Supervised and unsupervised learningEnsemble methodsDeep learning architectureOptimisation and probabilistic modelsEvaluate and select appropriate algorithms based on data characteristics, performance trade-offs, scalability, and interpretability requirementsApply knowledge of deep learning architectures such as:CNNs for vision use casesRNNs / LSTMs / GRUs for sequential dataTransformer architectures for NLP and structured dataFine-tuning and transfer learning approachesDrive experimentation frameworks, hypothesis testing, model validation, and statistical rigorEnsure robustness, generalisation, bias mitigation, and explainability in deployed modelsProvide technical direction on feature engineering strategies and model performance enhancementCollaborate with engineering teams to transition models into scalable production systemsMentor data scientists and uphold modelling standards, documentation, and reproducibility best practicesContribute to reusable ML frameworks, accelerators, and innovation initiativesRequired Experience & Qualifications15+ years of total professional experience, including8+ years of hands-on experience in machine learning and data scienceAdvanced degree (Master’s or PhD preferred) in Computer Science, Statistics, Mathematics, Engineering, or related quantitative disciplineProven experience building and deploying advanced ML and deep learning models in enterprise environmentsDeep understanding of algorithm selection, model complexity trade-offs, and overfitting/underfitting dynamicsStrong 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 themStrong SQL and data manipulation capabilitiesExperience working with large-scale datasets and distributed compute frameworks (e.g., Spark)Demonstrated ability to independently lead technical ML solution designExperience working in client-facing delivery environmentsExposure to cloud-based ML platforms (AWS, Azure, or GCP)Experience in NLP, Computer Vision, time-series forecasting, or optimisationExperience with fine-tuning large language models or foundation modelsFamiliarity with ML lifecycle management and monitoring practices