2 of 2 Statistical Inference Jobs in the City of London

driving customer decisions. The team leverages technology and AI to enhance, not replace, human creativity and insight. Responsibilities - Lead end-to-end execution of complex data science projects integrating statistical modelling, machine learning (ML), and deep learning (DL). - Collaborate closely with cross-functional teams (product, engineering, business stakeholders) to deliver data-driven solutions. - Design and conduct hypothesis testing … A/B and multivariate testing) and apply causal inference methodologies. - Perform exploratory data analysis, feature engineering, and develop models across traditional statistical and deep learning architectures. - Establish modelling frameworks with statistical quality control, detecting drift and decay. - Drive best practices for model explainability (e.g., SHAP/LIME) and scalable ML systems (including generative AI, NLP, CV … data science with at least 2 years leading teams. - Proven success in production deployment of ML/LLM/NLP/CV models. - Strong understanding of machine learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit-learn). - Experience with modern AI concepts: prompt engineering, embeddings More ❯

adopted and delivering measurable business value, supporting deal team members and portfolio company executives. We’re looking for data scientists who are passionate about impact—those who bring deep statistical knowledge, thrive in fast-paced environments, and want to see their models deployed, used, and making a difference. What you will do Build and deliver AI solutions: Design and … STEM field (or equivalent experience) with hands-on expertise in a least two of applied statistics, machine learning, forecasting, NLP, or optimization. Experience with uncertainty quantification, model evaluation, and statistical inference is highly valued. Python expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit … learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for analytics and modelling workflows. Model development & deployment: Familiarity with deploying models into production environments, collaborating with engineering teams, and using tools like MLflow or Weights & Biases for experiment tracking and More ❯