Senior Statistician

We're Hiring: Senior Statistician

Location: United Arab Emirates (Remote)

Employment Type: Full-Time

Experience Level: Senior

Work Arrangement: Fully Remote

About Us

We are a globally focused organization committed to using data, statistical evidence, and advanced analytics to support strategic planning, research, operational improvement, and informed decision-making across diverse markets.

Our multidisciplinary teams collaborate across Data, Research, Finance, Operations, Technology, Product, Commercial, Risk, Healthcare, Engineering, and Strategy to transform complex datasets into reliable insights and measurable business outcomes.

The Role

We are seeking an experienced Senior Statistician to lead statistical analysis, experimental design, predictive modeling, data interpretation, research methodology, and statistical consulting across organizational projects.

The ideal candidate will combine strong statistical expertise with practical business understanding to design rigorous analyses, develop statistical models, evaluate uncertainty, identify meaningful patterns, and communicate complex findings clearly to technical and non-technical stakeholders.

Key Responsibilities

• Develop and implement statistical methodologies, analytical frameworks, standards, and best practices.
• Lead statistical analysis projects from research design and data preparation through modeling, interpretation, reporting, and presentation.
• Translate business, research, operational, and scientific questions into appropriate statistical approaches.
• Design surveys, sampling methodologies, experiments, observational studies, and data-collection frameworks.
• Determine appropriate sample sizes, sampling methods, statistical power, and study designs.
• Develop sampling strategies for populations, customers, employees, products, transactions, locations, and other relevant groups.
• Conduct descriptive, inferential, multivariate, and predictive statistical analyses.
• Develop and validate statistical models using appropriate techniques and assumptions.
• Apply regression, classification, clustering, time-series, survival, multivariate, Bayesian, and other statistical methods where appropriate.
• Conduct hypothesis testing, confidence-interval analysis, variance analysis, correlation analysis, and statistical significance testing.
• Perform analysis of variance, covariance, experimental designs, and other appropriate analytical methods.
• Analyze longitudinal, panel, cross-sectional, time-series, and hierarchical datasets.
• Develop statistical forecasts and models to support planning, demand estimation, risk assessment, and operational decisions.
• Conduct predictive modeling and evaluate model performance using appropriate statistical measures.
• Perform model diagnostics, validation, sensitivity analysis, and robustness testing.
• Assess assumptions including independence, normality, homoscedasticity, linearity, stationarity, and other model-specific requirements.
• Identify potential sources of bias, confounding, measurement error, sampling error, and data-quality issues.
• Develop statistical controls and methods to improve data reliability and analytical validity.
• Clean, transform, structure, and validate datasets for statistical analysis.
• Work closely with Data Engineering and Data Science teams to ensure analytical datasets are fit for purpose.
• Establish data-quality checks, validation rules, analytical documentation, and reproducible workflows.
• Analyze large and complex datasets using statistical programming and analytical tools.
• Develop automated statistical reports, analytical pipelines, dashboards, and recurring analysis processes.
• Interpret statistical findings and translate results into clear business, research, and operational recommendations.
• Prepare statistical reports, research papers, technical documentation, executive summaries, and presentations.
• Communicate statistical concepts, assumptions, limitations, and findings clearly to non-technical stakeholders.
• Present analytical findings to senior management, project teams, researchers, clients, and other decision-makers.
• Provide statistical consulting and methodological guidance to internal teams.
• Review analytical methodologies developed by other analysts, researchers, and data professionals.
• Establish standards for statistical quality, reproducibility, documentation, and analytical integrity.
• Support research studies, market studies, customer analytics, operational studies, and performance evaluations.
• Design and analyze experiments, A/B tests, pilots, and controlled trials where applicable.
• Evaluate treatment effects, intervention outcomes, customer responses, and operational changes.
• Conduct causal-inference analysis using appropriate experimental or observational methodologies.
• Develop propensity, matching, regression, quasi-experimental, and other causal-analysis approaches where appropriate.
• Analyze customer behavior, market trends, operational performance, financial data, and other organizational datasets.
• Identify statistically significant patterns, relationships, trends, anomalies, and emerging risks.
• Distinguish meaningful statistical relationships from random variation and potential spurious correlations.
• Develop statistical indicators, benchmarks, confidence ranges, and performance measures.
• Establish analytical thresholds and monitoring approaches for significant changes or unusual patterns.
• Conduct statistical quality-control analysis and process-capability assessments where appropriate.
• Support forecasting, demand planning, resource allocation, financial analysis, and operational optimization.
• Work with Data Scientists and Machine Learning teams to evaluate model performance and statistical validity.
• Provide statistical expertise for machine-learning model development, validation, experimentation, and monitoring.
• Evaluate model bias, stability, generalization, uncertainty, and performance across relevant populations.
• Assess statistical implications of missing data, imbalanced datasets, outliers, and measurement inconsistencies.
• Develop appropriate approaches for missing-data treatment, imputation, outlier analysis, and data transformations.
• Ensure statistical analyses are appropriately documented, reproducible, auditable, and aligned with approved methodologies.
• Maintain statistical code, models, datasets, documentation, and analytical repositories.
• Select appropriate statistical software, programming languages, analytical platforms, and visualization tools.
• Monitor developments in statistical methodology, computational statistics, artificial intelligence, and advanced analytics.
• Evaluate new statistical techniques and tools and assess their practical applicability.
• Manage external statistical consultants, research partners, data providers, and specialist analytical resources where required.
• Mentor statisticians, analysts, researchers, and junior data professionals.
• Review analytical work and provide technical guidance to improve statistical rigor and quality.
• Promote evidence-based decision-making and responsible use of statistical information across the organization.
• Provide leadership with regular updates on statistical projects, analytical findings, research quality, risks, and improvement opportunities.

Key Performance Indicators

• Statistical analysis accuracy
• Analytical project delivery rate
• Statistical model performance
• Forecast accuracy
• Model validation completion
• Statistical methodology compliance
• Research design quality
• Sample-size and sampling accuracy
• Statistical power assessment completion
• Data-quality validation rate
• Analytical error rate
• Model diagnostic completion
• Reproducibility of statistical analyses
• Analytical documentation completeness
• Statistical report delivery timeliness
• Research project completion
• Experiment and A/B-test quality
• Experimentation cycle time
• Causal-analysis quality
• Predictive-model performance
• Model stability and robustness
• Statistical insight adoption
• Stakeholder satisfaction
• Executive-reporting quality
• Analytical automation rate
• Statistical process efficiency
• Data-quality issue resolution
• Bias and data-integrity monitoring
• Analytical methodology review completion
• Research reproducibility
• Statistical code quality
• Forecasting improvement
• Business decision-support contribution
• Cost and process improvement from analytics
• External consultant performance
• Junior analyst development
• Training and knowledge-sharing completion

Ideal Candidate

The successful candidate should have strong experience in statistics, applied statistics, biostatistics, econometrics, data analytics, quantitative research, statistical consulting, or advanced analytics, preferably within technology, financial services, healthcare, consulting, research, government, manufacturing, retail, or other data-intensive environments.

The candidate should demonstrate:

• Strong knowledge of statistical theory and applied statistical methodologies.
• Proven experience designing and executing complex statistical analyses.
• Strong understanding of probability, statistical inference, sampling, experimental design, and hypothesis testing.
• Advanced experience with regression, multivariate analysis, time-series analysis, forecasting, and predictive modeling.
• Experience designing surveys, experiments, observational studies, and sampling frameworks.
• Strong knowledge of statistical model development, validation, diagnostics, and interpretation.
• Experience identifying and addressing bias, confounding, missing data, outliers, and measurement error.
• Strong quantitative, analytical, and mathematical capabilities.
• Experience working with large and complex datasets.
• Strong statistical programming skills using tools such as R, Python, SAS, SPSS, Stata, or equivalent platforms.
• Strong proficiency with SQL and data-manipulation techniques is highly desirable.
• Experience with data visualization and communicating statistical findings effectively.
• Ability to explain complex statistical concepts to non-technical audiences.