7 of 7 Permanent Exploratory Analysis Jobs in London

production‐ready applications for internal teams and external clients. Identify and deliver automation opportunities, such as natural language interfaces over insurer datasets, AI‐assisted exploratory analysis, document understanding, and generation of structured analytical outputs. Apply rigorous AI evaluation and safety practices, including: Prompt and few‐shot experimentation Hallucination ...

conversations at scale. What You’ll Be Doing Analyse high-volume text and conversation data to uncover meaningful patterns and user behaviours Apply semantic analysis and NLP techniques to improve product performance and user experience Partner closely with product and engineering teams to translate insights into actionable decisions Build … scalable data pipelines and analytical frameworks using Python and SQL Take ownership of end-to-end analysis – from problem definition to recommendations and impact Influence product direction through insight-led, exploratory analysis , not just optimisation What We’re Looking For Proven experience working with large-scale text ...

conversations at scale. What You’ll Be Doing Analyse high-volume text and conversation data to uncover meaningful patterns and user behaviours Apply semantic analysis and NLP techniques to improve product performance and user experience Partner closely with product and engineering teams to translate insights into actionable decisions Build … scalable data pipelines and analytical frameworks using Python and SQL Take ownership of end-to-end analysis – from problem definition to recommendations and impact Influence product direction through insight-led, exploratory analysis , not just optimisation What We’re Looking For Proven experience working with large-scale text ...

result land with the people who'll act on it. Key Responsibilities Frame business questions as tractable data science and experimentation problems Conduct exploratory analysis, statistical modelling and hypothesis testing Build predictive and inferential models that drive real decisions Design and analyse experiments—A/B testing … Skills: Genuine curiosity about the problem behind the data Rigour in methodology, validation and reproducibility Excellent communication and data storytelling Strong business sense—connecting analysis to decisions and outcomes Comfortable with ambiguity and shaping unclear questions into structured work Collaborative approach across product, engineering and commercial teams Domain Flexibility ...

real-world datasets. Support SDTM, ADaM and TFL activities, including development, validation and review of study and standard programs for clinical trial data conversion, analysis and reporting. Produce validated datasets, tables, listings and figures in line with agreed standards, protocols, timelines and change control processes. Review and quality check … programming code, outputs and documentation to ensure accuracy, compliance and reproducibility. Collaborate with statisticians, data scientists and study teams to implement analysis plans, answer technical questions and contribute to programming best practices. Apply knowledge of GxP, ICH requirements, internal processes and external regulations while helping introduce innovative tools ...

real time, support forecasting, and shape commercial strategy. Their work is deployed in production code and has direct commercial consequences – it's more than exploratory analysis. The function is now embedding AI into how that work gets done. Successfully leading it will require close collaboration with engineering, product ...

sources independently to build datasets suited for modelling and in-depth analysis. Design and execute measurement frameworks— including A/B testing, causal impact analysis, and multivariate methods— selecting the appropriate technique based on the business question and clearly communicating trade-offs. Build, evaluate, and iterate on statistical models … scale. Proficient understanding of statistical concepts including regression, ANOVA, probability, and frequentist vs. Bayesian approaches, with the ability to distinguish statistically significant results from exploratory analysis. Experience applying a range of modelling techniques (e.g., linear and logistic regression, clustering) and iterating on models to improve accuracy and business relevance. ...