Senior Data Analyst

• Mission-Driven: We focus on creating economic and social impact.
• People-Centric: We care deeply about our team’s growth, well-being, and sense of belonging.
• Innovative: We embrace change and find better ways to do things together.
• Globally Connected: We foster collaboration between diverse cultures and perspectives.

• Conduct multi-dimensional analysis across accuracy, throughput, SLA adherence, workforce trends, queue performance, and financial or service-risk indicators.
• Distinguish natural performance variation from meaningful deviation using structured analytical methods.
• Identify likely drivers behind quality dips, adjustment spikes, instability patterns, and workstream deterioration.
• Use segmentation to isolate patterns across worker groups, task types, shifts, workflows, or use cases.
• Provide clear analytical summaries and practical recommendations to Quality and Delivery leadership.

• Apply practical statistical methods to test hypotheses, compare performance segments, and assess whether observed patterns are meaningful.
• Use sound reasoning around variance, distributions, trend interpretation, and sampling when analyzing operational data.
• Support structured intervention analysis where process or workflow changes need to be evaluated.
• Translate statistical findings into practical implications for operational stakeholders.

• Design and refine sampling approaches for system accuracy measurement, performance validation, and targeted investigations.
• Ensure sampling methods are representative, consistent, and aligned to the analytical purpose.
• Validate accuracy calculations, sample assumptions, and interpretation logic used in reporting and governance.
• Strengthen confidence in accuracy measurement practices across workstreams.

• Contribute to the definition and refinement of leading and lagging indicators at workstream level.
• Identify early signs of SLA instability, quality deterioration, throughput stress, rework patterns, or mismatch between internal metrics and client-observed outcomes.
• Improve the usefulness of internal performance measures in reflecting actual service experience.
• Contribute analytical support to at-risk workstream monitoring and related risk reviews.

• Build, validate, and improve dashboards, analytical views, and metric logic across workstreams.
• Use strong SQL and practical Python or R skills to extract, join, validate, and analyze raw operational data.
• Build and maintain advanced spreadsheet-based analytical models, formulas, validation logic, and lightweight scripts where reporting, control, or investigation workflows still rely on Google Sheets or Excel.
• Identify structural data gaps, inconsistent metric definitions, and reporting weaknesses that reduce trust in outputs.
• Partner with relevant teams to improve data quality, reporting consistency, and calculation clarity.

• Partner with Quality, Delivery, Workforce, Finance, and Technology stakeholders on complex performance-related analysis.
• Translate analytical findings into clear, actionable recommendations for business and operational leaders.
• Support enterprise initiatives such as RCA improvement, workflow redesign, incident analysis, and automation-related performance review through structured analysis.
• Operate effectively in ambiguous environments where data quality, definitions, or system logic may still be evolving.

• Provide review support, practical guidance, and analytical quality checks for junior analysts where needed.
• Help strengthen consistency in documentation, metric interpretation, and reporting logic across the team.
• Apply structured problem-solving methods, including DMAIC where relevant, to improve analytical repeatability and quality.
• Contribute to stronger statistical literacy and analytical consistency within Enterprise QSE through coaching, examples, and review feedback.

• 4–5 years of relevant experience in data analytics, business intelligence, performance analytics, or a related analytical role, ideally within operational, service, or production environments.
• Strong SQL skills, including joins, aggregations, trend analysis, and analytical querying.
• Hands-on experience using Python or R for data analysis, investigation, and manipulation.
• Strong spreadsheet capability, including advanced formulas, nested logic, lookup and array functions, cross-sheet modeling, validation controls, and lightweight scripting or automation in Google Sheets or Excel.
• Solid understanding of variance, distributions, sampling, and practical statistical interpretation.
• Ability to structure ambiguous operational problems into hypotheses, analysis paths, and recommendations.
• Ability to interpret leading and lagging indicators in operational performance environments.
• Ability to explain complex analysis clearly to non-technical stakeholders.
• Confidence working independently in evolving, ambiguous, and data-maturing environments.

• Experience designing or improving sampling and accuracy measurement approaches.
• Exposure to intervention analysis, forecasting, or performance risk monitoring.
• Familiarity with BI tools such as Looker, Tableau, or Power BI.
• Experience improving dashboard logic, reporting standards, or metric governance.
• Lean Six Sigma Yellow Belt or Green Belt, or familiarity with structured problem-solving methods.
• Relevant certifications such as Microsoft Data Analyst Associate (PL-300), advanced SQL certification, or statistical analysis coursework/certification.

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