ROLE SUMMARY

We are looking for a Senior Data Scientist with deep expertise across the full spectrum of AI/ML — including time series forecasting, computer vision, NLP, and Generative AI — to join our team. You will own the end-to-end lifecycle of machine learning solutions: from problem framing and exploratory analysis through model development, fine-tuning, deployment, and ongoing monitoring.

This is a hands-on, high-impact role for someone who is equally comfortable building production-grade deep learning pipelines and translating complex model outputs into clear business recommendations. You will work closely with engineering, product, and leadership to drive AI adoption across the organisation.

KEY RESPONSIBILITIES

Machine Learning & Predictive Modelling

• Design, develop, and deploy supervised and unsupervised ML models for classification, regression, clustering, ranking, and recommendation systems.
• Build end-to-end model training pipelines including feature engineering, hyperparameter tuning, cross-validation, and performance benchmarking.
• Conduct rigorous model evaluation, error analysis, and A/B testing; own model performance in production.
• Implement ensemble methods, gradient-boosted models (XGBoost, LightGBM, CatBoost), and deep learning architectures as appropriate.

Time Series Analysis & Forecasting

• Design and build scalable time series forecasting systems for demand planning, financial modelling, capacity planning, and operational metrics.
• Apply classical statistical methods (ARIMA, SARIMA, Exponential Smoothing, Prophet) alongside deep learning approaches (LSTM, GRU, Temporal Fusion Transformers, N-BEATS, DeepAR).
• Develop anomaly and change-point detection pipelines for real-time data streams.
• Handle challenges common to production time series: missing data, irregular sampling, multi-seasonality, hierarchical reconciliation, and cold-start forecasting.

Computer Vision

• Build and fine-tune convolutional and transformer-based vision models (ResNet, EfficientNet, Vision Transformers) for image classification, object detection, and segmentation tasks.
• Develop image processing and augmentation pipelines; work with labelling tools and annotation workflows.
• Apply transfer learning and domain adaptation techniques to achieve strong results with limited labelled data.
• Integrate vision models into production systems via APIs and edge-deployment where required.

Generative AI & Large Language Models

• Architect and implement GenAI solutions using Large Language Models (GPT-4, Claude, Gemini, LLaMA, Mistral) for enterprise use cases including summarisation, extraction, content generation, and intelligent agents.
• Design and build Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, FAISS) and embedding models.
• Fine-tune foundation models (LoRA, QLoRA, full fine-tuning) on proprietary datasets; manage training runs, evaluate outputs, and mitigate hallucination and bias.
• Develop advanced prompt engineering strategies, evaluation frameworks, and guardrails for safe, reliable generative outputs.
• Build agentic workflows and tool-use patterns using orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel).

Model Fine-Tuning & MLOps

• Fine-tune pre-trained models (language, vision, multimodal) for domain-specific tasks using parameter-efficient techniques (LoRA, adapters, prefix tuning) and full fine-tuning where appropriate.
• Manage experiment tracking, model versioning, and reproducibility using tools such as MLflow, Weights & Biases, or DVC.
• Build and maintain CI/CD pipelines for ML model deployment, monitoring, and automated retraining.
• Implement model observability: data drift detection, performance degradation alerts, and feedback loops.

Data Strategy & Stakeholder Engagement

• Translate ambiguous business problems into well-defined analytical frameworks with measurable outcomes.
• Partner with Data Engineering to design scalable data pipelines, feature stores, and data quality checks.
• Present model results, insights, and recommendations to senior leadership through clear data storytelling and visualisation.
• Mentor junior data scientists and analysts; contribute to team best practices, code reviews, and knowledge sharing.

REQUIRED QUALIFICATIONS

• 6–8+ years of professional experience in data science, machine learning, or applied AI.
• Master’s or PhD in Computer Science, Statistics, Mathematics, Physics, Engineering, or a related quantitative field (or equivalent industry experience).
• Strong proficiency in Python and the core ML stack: scikit-learn, TensorFlow / Keras, PyTorch, XGBoost, LightGBM.
• Proven track record in time series forecasting at scale — both classical and deep learning approaches.
• Hands-on experience with computer vision frameworks (OpenCV, torchvision, Detectron2, or equivalent).
• Demonstrated experience building applications with LLMs: prompt engineering, RAG, fine-tuning (LoRA/QLoRA), and evaluation.
• Solid grounding in statistical inference, experimental design, Bayesian methods, and causal reasoning.
• Experience with cloud ML services (AWS SageMaker, GCP Vertex AI, Azure ML) and containerised deployments (Docker, Kubernetes).
• Proficiency with SQL and large-scale data processing (Spark, Databricks, BigQuery, or equivalent).
• Familiarity with MLOps tooling: MLflow, Weights & Biases, Kubeflow, or similar.

PREFERRED QUALIFICATIONS

• Experience with multimodal models and architectures (vision-language models, diffusion models).
• Familiarity with reinforcement learning, RLHF, or reward modelling techniques.
• Hands-on experience with speech/audio processing or signal processing pipelines.
• Published research, patents, or notable open-source contributions in ML/AI.
• Experience working in regulated industries (finance, healthcare, energy) with model governance and explainability requirements (SHAP, LIME, Integrated Gradients).
• Knowledge of responsible AI practices, fairness auditing, and emerging AI regulatory frameworks.