1 to 25 of 57 Permanent Statistics Jobs in London

methods to solve unique financial challenges. Commit to continuous learning in financial analytics, tools, and best practices to enhance data analysis capabilities. Qualifications: Bachelor’s degree in Finance, Economics, Statistics, Mathematics, or a related field. Strong foundational knowledge of data analysis, with a keen interest in developing further expertise in the finance sector. Proficiency in Excel is required; familiarity with More ❯

on technical depth in Python (or R), cloud platforms, and modern ML frameworks. Experience mentoring or leading small, high-performing teams. PhD or equivalent advanced degree in Data Science, Statistics, Computer Science, or related quantitative discipline. Recognized as a technical thought leader through publications, external talks, or open-source contributions. Preferred Experience: Experience using Palantir platforms (Foundry, AIP, Ontology) to More ❯

and experimentation. Enjoy autonomy, ownership, and creative freedom-your ideas will have visible impact. Please click here to view the full job description Essential Skills & Experience: Strong fundamentals in statistics, probability, mathematical modeling, and computational thinking. Deep understanding of machine learning algorithms, optimization, architectures, and evaluation. Experience with data engineering workflows, including ETL, pipeline design, and feature extraction. Proficiency in More ❯

Power BI, or similar tools is an advantage. Ability to communicate complex findings clearly and concisely. Well-organised with strong problem-solving and time management skills. Degree in Mathematics, Statistics, Economics, Computer Science, or a related discipline preferred (not essential). Up to 2 years’ experience in a data-related or analytical role — graduates are welcome to apply. What’s More ❯

development of analytics models to predict trends and behaviors. Engage in continuous learning to stay abreast of the latest analytics technologies and methodologies. Qualifications: Bachelor's degree in Mathematics, Statistics, Computer Science, or a related field, with a strong academic record. Demonstrated interest in data analysis through coursework, projects, or internships. Proficiency in at least one of the following: SQL More ❯

management abilities. Preferred Qualifications: Experience in alternative data usage for credit risk evaluation. Familiarity with Open Banking and fintech lending platforms. Bachelor's or Master's degree in Finance, Statistics, Mathematics, Economics, or a related field. Benefits Pension Plan Paid Time Off Work From Home Training & Development #LI-A More ❯

members, and may manage junior Data Scientists or graduates in the future. Who you are You hold a Master's degree in a data-relevant field such as Maths, Statistics, Data Science, Engineering or Psychology and have experience in data science in a commercial environment, and have demonstrated leadership in that role. With experience in relevant coding languages (Python or More ❯

expect you to spend time on site (at our offices or a client location) for collaboration sessions, customer meetings, and internal workshops. What You'll Do Strong grasp of statistics and probability fundamentals Solid understanding of machine learning algorithms for supervised and unsupervised learning Understanding of Transformer based models Experience developing AI agents Strong Python and SQL skills Experience with More ❯

Ability to build, evaluate, and deploy ML models (REST APIs, batch jobs, FastAPI, etc.) Experience operating in cloud environments (AWS preferred) using Docker and Git workflows Strong foundation in statistics, machine learning, and experimental design Proven ability to communicate technical solutions clearly to non-technical stakeholders Technical skills (a big plus): Knowledge of deep learning frameworks (PyTorch, TensorFlow), transformers, or More ❯

and results to both technical, and business audiences Ability to work both independently and in highly collaborative team environments Preferred qualifications, capabilities, and skills Strong background in Mathematics and Statistics Familiarity with the financial services industries Published research in areas of natural language processing, deep learning, or reinforcement learning at a major conference or journal Ability to develop and debug More ❯

Legal Data Analytics Manager Location: London Permanent role. Primary skills Data statistics & Analysis; Client Dashboards;To extract Data, consolidate and analyse it; Create automation solutions including AI solutions; Quantitative analytics (e.g. offer calculations for claims); Analysis for Decision making; Modelling of quantum exposure; Record Data breach analytics The Data Analytics Manager will focus on building internal client relationships and on More ❯

extensive practical experience of using innovative platforms for data visualization including R Shiny, Power BI and/or Tableau A degree or equivalent professional experience in subjects such as Statistics, Data/Computer Science, Economics, Public Policy, or the Sciences would be desirable Please see attached Job Description and Person Specification for further details. About Us: Victim Support (VS) is More ❯

such as financial services, insurance, and operations optimization. This is a high-growth opportunity for someone with early industry experience (or strong academic grounding) in data science and applied statistics, eager to deepen their expertise and grow within a dynamic AI team working at the frontier of applied analytics and machine learning. Key Responsibilities: Contribute to the development of predictive … fast-paced, evolving environments. Strong written and verbal communication skills, with the ability to translate technical findings into business recommendations. Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Economics, or another quantitative discipline. Preferred experience: Hands-on experience with Palantir platforms (Foundry, AIP, Ontology), including developing analytical workflows and deploying insights within enterprise environments. PhD in … Data Science, Statistics, Computer Science, or a related quantitative field. Publications in top data science/ML conferences or journals (e.g., NeurIPS, ICML, KDD, ACL, or similar). Open-source contributions to the data science or ML community (libraries, notebooks, packages, or tutorials). Experience presenting research or applied work at meetups, workshops, or industry conferences. Familiarity with vector databases More ❯

and open-source projects Mentor junior researchers and help shape technical strategy Participate in the full research lifecycle from ideation to deployment Requirements: PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related field Strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) 2-5 years of post-PhD experience Deep expertise in machine More ❯

Are highly skilled in Python and Jupyter notebooks. Have experience using libraries including numpy, pandas, scipy, sympy, scikit-learn, torch, tensorflow. Have a bachelor's degree in data science, statistics, computer science, or related field in the U.S., Canada, New Zealand, UK or Australia. Have a strong background in one or more of the following areas: exploratory data analysis and More ❯

and finding creative solutions Bachelors Degree Required, ideally in Information Sciences or Business Studies Excellent knowledge of Microsoft Office, including Excel and PowerPoint. Data analysis skills essential; knowledge of statistics modelling is preferred. Experience with analytics tool such as Tableau, SQL, R, Python or MATLAB is a plus. Client-facing experience is a plus. Benefits Hybrid working (2 days in More ❯

Python) You have an understanding of data pipeline concepts (e.g. ETL, scripting common analysis workflows) It'd be a bonus if you: Have obtained a degree ideally related to Statistics, Mathematics, Computer Science, etc (degree subject not mandatory - but successful candidates will demonstrate high levels of fluency in data, data analytics and data decision making) Have experience with Airflow or More ❯

hour windows, 2–3 times per week) to review trading simulations, debug models, and exchange quantitative and technical insights. Requirements Strong academic or professional background in Applied Mathematics, Statistics, Computer Science, Physics, Finance, or Quantitative Trading. Deep understanding of market microstructure, high-frequency trading systems, probability, optimisation, and time-series analysis. Proficiency in one or more programming languages commonly used More ❯

proven client-facing experience. Strong quantitative and analytical skills, with experience in marketing mix modeling, multi-touch attribution, customer segmentation, or identity/data management platforms. A degree in Statistics, Data Science, Marketing Analytics, or a related field. Excellent communication skills, with the ability to simplify complex data into actionable insights for diverse audiences. Proficiency in Excel and PowerPoint; familiarity More ❯

and compliance through accurate data analysis Identifying opportunities for process automation and efficiency improvements Experience and skills required: Data analysis experience Preferably a degree in the following subjects: Mathematics, Statistics, Actuarial Science, or related field Advanced Excel skills SQL experience Knowledge of BI tools, e.g. Power BI, SAP Business Objects, would be advantageous The ability to present complex data in More ❯

Plan and document work methodically to ensure clarity. What We're Looking For Proven experience in a Data Analyst role. Degree in a quantitative field (e.g., STEM, Mathematics, Engineering, Statistics). Strong SQL skills. Solid Power BI experience with a focus on dashboard design and UX. Good DAX experience. Highly organised with excellent attention to detail. Strong communicator able to More ❯

consumer or marketplace environments. Experience developing analytics, machine learning, and AI solutions for CRM and personalization, including propensity, churn, LTV/CLV, uplift, or recommendation models. Strong experimentation and statistics skills, including test design, sample sizing, variance reduction techniques, and causal inference. Proficiency in SQL and Python for data wrangling, modeling, and automation, with familiarity in data warehouses and version More ❯

proven client-facing experience. Strong quantitative and analytical skills, with experience in marketing mix modeling, multi-touch attribution, customer segmentation, or identity/data management platforms. A degree in Statistics, Data Science, Marketing Analytics, or a related field. Excellent communication skills, with the ability to simplify complex data into actionable insights for diverse audiences. Proficiency in Excel and PowerPoint; familiarity More ❯

will be collected anonymously, in confidence and will not be seen by anyone directly involved in the hiring process, it will be stored separately and used only to provide statistics for monitoring purposes. There is no obligation on you to provide information. All applicants will be treated the same, whether or not they provide this information. #INDPREM More ❯

and external regulations. Mentor and guide other data scientists, fostering technical excellence, innovation, and collaboration across the team. Requirements Qualifications: 8+ years of experience in data science or applied statistics roles, with a proven track record of driving measurable business impact. Expertise in one or more of supervised and unsupervised machine learning, deep learning, time series analysis, causal inference, and … collaborative tools (Databricks, Snowflake) is a plus. Exceptional ability to translate technical results into clear, actionable insights for senior executives and non-technical audiences. Master's or PhD in Statistics, Data Science, Computer Science, Economics, or a closely related discipline. Preferred Qualifications: Experience working with Palantir platforms (Foundry, AIP, Ontology) to develop, analyze, and operationalize data-driven insights within enterprise … scale environments. PhD in Data Science, Statistics, Computer Science, or a related quantitative discipline. Publications in top-tier AI/ML or data science conferences or journals (e.g., NeurIPS, ICML, KDD, AAAI, ACL, JASA). Recognized contributions to the open-source data science/ML ecosystem (e.g., libraries, frameworks, toolkits, widely adopted notebooks). Track record of thought leadership through More ❯