Data Scientist Job London area,Greater London England UK,IT/Tech

Location: Greater London

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
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/under fitting dynamics
Strong proficiency in Python and ML ecosystems (scikit‑learn, pandas, Num Py)
Experience with deep learning frameworks (PyTorch or Tensor Flow)
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

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