Senior Statistician

About Us

We are a globally oriented organization dedicated to leveraging data, statistical evidence, and advanced analytical techniques to enhance strategic planning, research initiatives, operational excellence, and informed decision-making across various markets. Our interdisciplinary teams work collaboratively across domains such as Data, Research, Finance, Operations, Technology, Product, Commercial, Risk, Healthcare, Engineering, and Strategy to convert complex data into actionable insights and tangible business value.

Role Overview

We are looking for a seasoned Senior Statistician to spearhead statistical analyses, experimental designs, predictive modeling, data interpretation, research methodologies, and provide expert statistical consulting for organizational projects. The ideal candidate will blend deep statistical knowledge with practical business insight to develop rigorous analyses, create and validate statistical models, assess uncertainties, detect meaningful data patterns, and clearly convey complex results to both technical and non-technical audiences.

Key Responsibilities

• Create and apply statistical methods, analytical frameworks, standards, and best practices.
• Lead projects encompassing research design, data preparation, modeling, interpretation, reporting, and stakeholder presentation.
• Convert business, research, operational, and scientific inquiries into suitable statistical methods.
• Design surveys, sampling techniques, experiments, observational studies, and data capture structures.
• Define sample sizes, sampling approaches, statistical power, and study designs appropriately.
• Devise sampling strategies targeting populations, customers, employees, products, transactions, locations, and other key segments.
• Perform descriptive, inferential, multivariate, and predictive statistical analyses.
• Develop and validate statistical models using appropriate techniques and assumptions.
• Apply various statistical methods including regression, classification, clustering, time-series, survival, multivariate, Bayesian where fitting.
• Conduct hypothesis testing, confidence intervals, variance and correlation analyses, and evaluate statistical significance.
• Use analysis of variance (ANOVA), covariance, and experimental designs effectively.
• Analyze longitudinal, panel, cross-sectional, time-series, and hierarchical datasets.
• Build forecasts and statistical models aiding planning, risk evaluation, demand estimation, and operational decision-making.
• Carry out predictive modeling and assess model performance using pertinent measures.
• Conduct model diagnostics, validation, sensitivity checks, and robustness testing.
• Evaluate assumptions like independence, normality, homoscedasticity, linearity, stationarity, and other model prerequisites.
• Identify sources of bias, confounding, measurement and sampling errors, and data quality issues.
• Develop controls to enhance data reliability and analytical validity.
• Prepare, clean, transform, structure, and verify datasets for analysis.
• Collaborate closely with Data Engineering and Data Science teams to ensure quality analytical datasets.
• Implement data-quality validations, document analysis procedures, and maintain reproducible workflows.
• Analyze large complex datasets using statistical programming and analytic instruments.
• Create automated statistical reports, pipelines, dashboards, and repeatable analytical processes.
• Interpret analysis results into clear, actionable insights for business, research, and operations teams.
• Produce detailed reports, technical papers, executive summaries, and presentations on statistical findings.
• Explain statistical theories, assumptions, limitations, and results to non-technical audiences.
• Present insights to senior executives, project groups, researchers, clients, and decision-makers.
• Offer statistical consultation and methodological advice internally.
• Review analytics work from other analysts to ensure quality and rigor.
• Set standards for statistical quality, reproducibility, documentation, and analytical integrity.
• Support varied studies including market research, customer analytics, operational performance, and program evaluations.
• Design and analyze experiments, A/B tests, pilots, and controlled trials where applicable.
• Assess treatment effects, intervention outcomes, and customer or operational responses.
• Apply causal inference methods using experimental and observational approaches.
• Utilize approaches like propensity scoring, matching, regression, and quasi-experimental designs.
• Analyze customer behavior, market dynamics, financial data, and other organizational information assets.
• Identify significant patterns, trends, anomalies, and emerging risks from data.
• Distinguish meaningful relationships from random variations and false correlations.
• Develop benchmarks, indicators, confidence intervals, and performance metrics.
• Set analytical thresholds and monitoring systems for unusual changes or patterns.
• Conduct quality control and capability assessments on statistical processes.
• Assist in forecasting, demand planning, financial analysis, resource management, and operational optimization.
• Work with Data Scientists and ML teams to evaluate model performance and statistical soundness.
• Provide statistical expertise during machine learning model development, validation, experimentation, and monitoring.
• Assess model bias, stability, generalization, uncertainty, and performance across populations.
• Analyze missing data, imbalanced samples, outliers, and measurement inconsistencies.
• Develop solutions for addressing missing data, imputations, outlier evaluations, and data transformations.
• Ensure analyses are well documented, reproducible, auditable, and adhere to established protocols.
• Maintain statistical codebases, models, datasets, documentation, and repositories.
• Select suitable statistical software, programming languages, analytical platforms, and visualization tools.
• Stay abreast of advances in statistical methodology, computational statistics, AI, and advanced analytics.
• Assess the practicality of new statistical techniques and technologies.
• Manage external analytic consultants, research collaborators, data suppliers, and specialty resources as needed.
• Mentor junior statisticians, analysts, and researchers.
• Review and guide analytical work to promote methodological rigor and quality.
• Advocate for evidence-based decision-making and responsible statistical practices organizational-wide.
• Keep leadership informed on project progress, research quality, risks, and opportunities for improvement.

Performance Metrics

• Accuracy of statistical analyses
• Timely delivery of analytical projects
• Performance of statistical models
• Precision of forecasts
• Completion of model validations
• Adherence to statistical methodologies
• Quality of research designs
• Accuracy in sample size and sampling
• Completion of statistical power analyses
• Data quality validation rates
• Minimization of analytical errors
• Thoroughness of model diagnostics
• Reproducibility of analyses
• Completeness of analytical documentation
• Timeliness of report delivery
• Research project completion
• Quality of experiments and A/B testing
• Duration of experimentation cycles
• Quality of causal analyses
• Success of predictive models
• Robustness and stability of models
• Adoption of statistical insights by stakeholders
• Stakeholder satisfaction
• Quality of executive reporting
• Automation level of analytics
• Efficiency of statistical processes
• Resolution of data quality problems
• Monitoring of bias and data integrity
• Completion of methodology reviews
• Reproducibility of research
• Quality of statistical coding
• Improvement in forecasting
• Contribution to business decision support
• Cost and process enhancements from analytics
• Performance of external consultants
• Development of junior analysts
• Completion of training and knowledge sharing

Ideal Candidate Profile

• Extensive experience in statistics, applied statistics, biostatistics, econometrics, data analytics, quantitative research, or statistical consulting, especially in technology, financial services, healthcare, consulting, research, government, manufacturing, retail, or other data-driven fields.
• Solid grounding in statistical theory and practical application of diverse statistical methodologies.
• Proven track record designing and implementing sophisticated statistical analyses.
• Strong expertise in probability, inference, sampling, experimental design, and hypothesis testing.
• Advanced skills in regression, multivariate analysis, time-series methods, forecasting, and predictive modeling.
• Proficiency in designing surveys, experiments, observational studies, and sampling frameworks.
• Competence in model development, validation, diagnostic assessments, and interpretation.
• Experience in identifying and correcting bias, confounding factors, missing data problems, outliers, and measurement errors.
• High quantitative, analytical, and mathematical skill level.
• Familiarity with handling large, complex datasets.
• Advanced ability to program statistically using R, Python, SAS, SPSS, Stata, or similar platforms.
• Strong SQL skills and ability to manipulate data effectively is a plus.
• Experience creating visualizations and conveying statistical results clearly to varied audiences.
• Capacity to explain intricate statistical concepts to laypersons with clarity.