Research jobs in London

What you would do

• Pick up samples from clients and deliver them to SGS laboratories following a pre-defined route.
• Provide courteous, efficient, and professional sample pickup and delivery of sampling supplies.
• Complete log sheets and obtain customer signatures for chain of custody and billing.
• Take sample temperatures upon receipt.
• Maintain the company vehicle in good order and clean condition.
• Maintain a safe driving record and complete defensive driver training as required.

Skills

What you would do

• Set the technical and research direction for a new real-world AI systems team.
• Work directly with the customer to build a healthy, productive technical relationship.
• Design, supervise, and analyse real-world field experiments.
• Lead hands-on research and engineering work to improve how AI systems perform with messy real-world data.
• Build, manage, and mentor a growing team of researchers and engineers.
• Help recruit team members as the team grows.

Skills

What you would do

• Execute and analyze advanced computational simulations (DFT, DFPT, MD) to predict semiconductor properties like band gaps, defect levels, leakage currents, dielectric constants, and interfacial properties.
• Apply physical and chemical intuition to semiconductor materials discovery problems, analyzing structure-property relationships at atomic and interface scales.
• Develop and manage computational workflows using Python for data analysis and tool automation.
• Identify candidate semiconductor materials computationally and collaborate with experimentalists to synthesize them in the lab.
• Develop or apply machine learning models for materials property prediction.
• Run simulations on HPC or cloud infrastructure.

Skills

What you would do

• Improve the chatbot's quality and expand the account actions it can complete autonomously.
• Expand in-house document extraction pipelines to make parsing faster and more accurate.
• Design LLM-driven workflows that simplify case management.
• Find new opportunities to replace manual work with effective AI systems.
• Own solutions from discovery and prototyping through deployment, evaluation and monitoring.

Skills

What you would do

• Analysing high-throughput genomic and spatial data, including whole genome sequencing, bulk RNA-seq, single-cell RNA-seq, peripheral cytokine, spatial proteomics and transcriptomics.
• Applying bioinformatics and statistical approaches to clinical and experimental datasets.
• Using R/Bioconductor and/or Python to analyse and interpret data.
• Working with Linux, cloud and high-performance computing (HPC) environments.
• Integrating publicly available NGS datasets with data generated within the lab.
• Working with scientists to interpret findings and answer biological questions.

Skills

What you would do

• Build and improve data science products across pricing, recommendations and other business domains.
• Turn ambiguous business problems into practical data science solutions, from exploration and modelling through to experimentation and evaluation.
• Partner with engineers, analysts and product teams to deploy reliable models and embed recommendations into everyday decision-making.
• Monitor model performance, investigate changes and continuously improve solutions as customer behaviour and business needs evolve.
• Communicate findings clearly, challenge assumptions and help teams make confident, evidence-based decisions.
• Develop predictive models for pricing and recommendations.

Skills

What you would do

• Map the microprotein landscape across three stages of infection (early, peak, resolution) in mouse epithelial cell lines and C57BL/6 mouse models using a dual prokaryotic-eukaryotic Ribo-seq and proteomics workflow.
• Probe functions of infection-induced microproteins using proximity labeling (BioID) to map interaction partners.
• Develop and validate a dual prokaryotic-eukaryotic microprotein discovery workflow, including thermal proteome profiling (TPP) and AI-driven peptide-spectrum matching refinement.
• Collaborate across the ORFeus network, providing dual-species training data for computational models and contributing datasets to the shared ORFeome platform.
• Produce the project's scientific report.
• Spend ~3 months at Tesorai refining AI-driven peptide-spectrum matching for microprotein identification.

Skills

What you would do

• Lead and deliver user research for complex public sector projects, ensuring findings inform product, service, and strategy decisions.
• Design and run generative and evaluative studies, including usability testing, depth interviews, surveys, co-design, and diary studies.
• Analyse and synthesise findings into clear, actionable insights.
• Contribute to client meetings, workshops, and presentations, building client relationships and providing recommendations.
• Advise on research strategy and influence decisions at senior levels.
• Mentor and upskill junior researchers and support the community of practice.

Skills

What you would do

• Modelling, implementing and maintaining the FX volatility analytics framework.
• Developing risk and analytical tools for Portfolio Management decision-making.
• Contributing to the cross-asset analytics platform.
• Delivering projects from start to finish.

Skills

What you would do

• Develop predictive and prescriptive models and ad hoc analyses to support business decision-making.
• Work end-to-end on data solutions, from understanding complex business challenges to designing and implementing them.
• Work with large and small datasets, including third-party and internal data.
• Apply machine learning and generative AI techniques to solve business problems.
• Collaborate with data scientists, product, and business teams.
• Communicate the business value of analytical solutions to stakeholders.

Skills

What you would do

• Work as part of a cross-functional product squad to launch and scale AI client solutions, from core data science products (pricing and forecasting) to Agentic AI.
• Design and advise on the data science approach for products, ensuring models are reusable across client contexts.
• Partner with sector and go-to-market teams to identify client challenges and demonstrate product capabilities.
• Collaborate with engineers to productionise models on cloud platforms using MLOps and DevSecOps practices.
• Monitor model performance and user feedback to refine algorithms and enhance feature design.
• Embed responsible and explainable AI principles into development.

Skills

What you would do

• Lead strategic discovery across multiple product teams from plan to validated view of customer value.
• Pick research methods (Jobs-to-be-Done, Kano, MaxDiff, interviews, usability testing) to build evidence bases for strategic decisions.
• Combine qualitative insight with analytics and behavioural data.
• Build scalable and repeatable Research Ops including processes, methods, governance, forums, roles and responsibilities.
• Set research standards for recruitment, methods, ethics, synthesis and reporting.
• Coach designers and clients to run effective strategic and tactical research.

Skills

What you would do

• Drive and contribute to cutting-edge research projects in LLM/Agent/NLP.
• Develop benchmarks and baselines using publicly available datasets.
• Train large language models (LLMs) on innovative, practical tasks.
• Publish research findings at leading NLP/ML/AI conferences and in top journals.

Skills

What you would do

• Design, develop, and deploy AI/ML solutions supporting Markets Operations.
• Develop and implement advanced machine learning models and algorithms to address operational challenges.
• Deploy AI/ML solutions for process automation, anomaly detection, document intelligence, and workflow optimization.
• Architect and oversee generative AI applications to automate and enhance business processes.
• Build and maintain production-grade AI/ML applications and tools for Market Operations.
• Monitor, evaluate, and improve the performance of deployed models and solutions.

Skills

What you would do

• Lead the development and deployment of machine learning models and data science solutions to improve Receive product performance across different Wise markets.
• Analyse large volumes of customer, transaction and product data to identify trends, patterns, risks and opportunities.
• Design and implement experiments to evaluate the effectiveness of product changes, decisioning systems and customer experience improvements.
• Build scalable modelling approaches supporting prioritisation, personalisation, risk management and operational decision-making.
• Collaborate with analysts, product managers, engineers, operations and risk teams to translate business requirements into data science solutions.
• Develop robust data pipelines, algorithms and tools for production-grade modelling.

Skills

What you would do

• Develop statistical fraud models and scores (consumer fraud, commercial fraud, synthetic fraud, payment intent models).
• Blend data sources including consumer bureau data, postcode insights, and transactional data to improve model predictiveness.
• Apply traditional techniques like logistic regression and modern AI/ML methods such as gradient boosting, random forests, and clustering.
• Design and run champion/challenger tests for new fraud scores using cloud-based tooling.
• Create new characteristics and attributes to maximize model predictive power.
• Investigate segmentation and sub-populations to optimize score performance.

Skills

What you would do

• Build and maintain ML models across the core portfolio: CLTV, churn prediction, propensity to buy, and Next Most Likely Product (NMLP).
• Develop fraud detection models including transaction-level classifiers, merchant behaviour anomaly detectors, and new-account risk scorers.
• Contribute scored model outputs to the Next Best Action (NBA) decisioning layer.
• Support A/B experiments, uplift tests, and multi-armed bandit evaluations.
• Design and implement end-to-end ML pipelines from data ingestion and feature engineering through training, evaluation, and deployment.
• Monitor deployed models in production for performance degradation, data drift, and data quality issues.

Skills

What you would do

• Build processing pipelines that take laser ranging data from raw returns to normal points and higher-level products.
• Develop statistical methods for bias detection, outlier rejection and data quality.
• Analyse station and network performance and identify improvements.
• Collaborate with the geodesy and orbit determination team to turn research into products.
• Produce reports, plots and customer-facing metrics from analysis.

Skills

What you would do

• Build, validate and monitor application and behavioural scorecards using bureau, Open Banking and internal data.
• Design, test and refine credit strategy including cut-offs, affordability rules, pricing tiers and limits.
• Run experiments and champion/challenge tests to prove policy and model changes before scaling.
• Explore new data sources and alternative signals and build the case for their use in decisioning.
• Support model governance, documenting fairness evidence and meeting Consumer Duty, CONC and model risk obligations.
• Work with engineers to deploy models and rules into production and use AI tooling to improve team efficiency.

Skills

What you would do

• Provide data-driven analysis and insights to product desks and business partners to support decision-making.
• Conduct strategic and complex data analysis, identifying trends, insights and business implications, and translating findings into actionable recommendations.
• Analyse and interpret data across multiple platforms and data sources to identify optimisation opportunities and improve data quality.
• Partner with users and clients to assess and resolve complex system and process-related issues, recommending effective solutions.
• Act as a key liaison between business stakeholders and Technology, communicating complex requirements and solutions.
• Mentor and technically lead Associate and Analyst-level quantitative professionals, conducting code reviews and guiding methodology.

Skills