1 to 25 of 191 Statistical Analysis Jobs

Senior Statistical Analyst London Contract – Maternity cover, 12 months fixed term Working hours – Full time (35 hours per week). Standard working hours between 9am – 5pm. Working arrangements – London, hybrid homeworking (minimum 6 days a month in office). We are seeking to recruit a confident and enthusiastic Senior … Statistical Analyst with proven ability to work comfortably on their own and as part of a team. They will work on statistical analysis projects, having expertise in improving data quality, data analysis, data reporting, and communicating statistics and trends to a lay audience. The person will ...

Description Senior Statistical Analyst London Contract – Maternity cover, 12 months fixed term Working hours – Full time (35 hours per week). Standard working hours between 9am – 5pm. Working arrangements – London, hybrid homeworking (minimum 6 days a month in office). We are seeking to recruit a confident and enthusiastic … Senior Statistical Analyst with proven ability to work comfortably on their own and as part of a team. They will work on statistical analysis projects, having expertise in improving data quality, data analysis, data reporting, and communicating statistics and trends to a lay audience. The person ...

seeking a Senior Data Analyst/Equal Pay Specialist to lead the technical delivery of the Equal Pay Audit. This role involves undertaking advanced statistical analysis of workforce and pay data to identify, quantify, and explain pay differentials, ensuring that outputs are robust, transparent, and defensible. … Role: Lead pay modelling and statistical analysis, including mean, median, and weighted pay comparisons across employee groups. Analyse pay outcomes across protected characteristics (gender, ethnicity, disability) and other demographics where data allows. Identify statistically and practically significant pay gaps, trends, and risk areas. Assess differentials to determine whether ...

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Professional Experience: Track record of successful delivery in a statistical analysis or modelling role, preferably in a Banking environment (Required) Proven ability of conducting statistical analysis in a coding environment such … regulatory compliance requirements such as Basel or IFRS9 (Preferred) Education: Degree level education in a quantitative field (Required) Hard Skills: Statistics: Knowledge of statistical techniques such as logistic regression, linear regression, time series analysis, and survival analysis (Required) Programming: Proficiency in statistical programming languages such ...

identify trends, risks and opportunities that support commercial performance and strategic decision-making. Ensure data accuracy, consistency and integrity across reporting processes. Data Analysis & Commercial Insight Conduct detailed analysis and statistical modelling to identify patterns, trends and anomalies across the business, including but not limited to: Category … performance and trend analysis Promotional and campaign effectiveness Seasonal trend analysis and forecasting Claims trend identification and recommendation of corrective actions Budget modelling and forecasting support Proactive sales performance analysis Private Label performance analysis by market to identify growth opportunities Identification of new business and market ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks). Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds … modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. Document problem statements clearly with realistic business contexts and provide verified correct answers. How to get started Simply apply to this post, qualify ...

deploy advanced analytics, machine learning models, and data products Lead and contribute to high-impact data science projects across the organisation Translate complex analysis into clear, actionable insights and recommendations Work closely with data engineers to ensure robust, scalable data pipelines and solutions Provide technical leadership , setting standards … analytics Contribute to best practice in data science, governance, and ethical use of data Tech & Tools You'll typically work with: Python and advanced statistical tools Machine Learning and AI frameworks Cloud-based data platforms (e.g. Azure ecosystems) Data visualisation and reporting tools Data engineering and transformation pipelines What ...

identify patterns, trends, and actionable insights. Support the development and evaluation of machine-learning models. Assist with data cleaning, preparation, and feature engineering. Conduct statistical analysis to support business decisions. Build reports, dashboards, and visualisations for internal stakeholders. Collaborate with technical and non-technical teams to translate data … above ideally from a Redbrick university (e.g., Russell Group or equivalent) Strong programming skills in Python and R. Solid understanding of statistical analysis and quantitative methods. Foundational knowledge of machine learning techniques (classification, regression, clustering, etc.). Strong analytical mindset and attention to detail. Ability to present findings ...

operate with high autonomy and influence decision-making across cross-functional teams. Key responsibilities Search Evaluation & Analytics Perform a leading role in analysis of search and retrieval system performance, including ranking quality and relevance. Help define and standardize search evaluation metrics (e.g., NDCG, MAP, recall, precision, CTR). Analyze … testing and experimentation frameworks for search and ranking improvements. Partner with product and data science to define success metrics and experiment strategies. Ensure statistical rigor in experiment design, analysis, and interpretation. Build reusable experimentation templates and scalable analysis workflows. Data Visualization & Communication Own and evolve dashboards ...

begin a career in data and business intelligence. Working within the Service Performance Team, you'll gain hands-on experience across the full data analysis lifecycle, from collecting and validating data through to creating dashboards, reports, and insights that support decision-making. You'll develop practical skills in data … analytics, data quality, stakeholder communication, and Power BI development. Key Responsibilities Data Analysis & Reporting Gather, analyse, and interpret data from a range of sources Produce reports, statistical analysis, and performance insights Identify trends, patterns, and opportunities to improve service performance Support the production of forecasts and performance ...

driving data-backed decision-making in a high-impact environment. Key Responsibilities Collect, clean, and analyze structured and unstructured data Perform exploratory data analysis (EDA) to identify trends and patterns Build and maintain dashboards and reports for business insights Write efficient SQL queries to extract and manipulate data Assist … Preferred Qualifications Experience with real-world datasets or analytics projects Knowledge of business metrics and performance tracking Exposure to A/B testing or statistical analysis Familiarity with startup or fast-paced environments Key Skills Data Analysis & Interpretation SQL & Data Querying Dashboarding & Visualization Critical Thinking Communication & Storytelling ...