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# Supervisor, Data Analytics

- **Company:** McDonald's
- **Location:** Chicago, IL, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Microsoft Excel, Big Data, Customer Data Management, Information Engineering, Data Structures, Data Visualization, Python (Programming Language), Microsoft Office, Regression Analysis, Operational Data Store, Microsoft PowerPoint, Power BI, SQL Databases, Tableau (Software), Data Processing, Information Technology, Data Analytics
- **Published:** September 4, 2026
- **Apply:** https://www.chicagocareersite.com/job.asp?id=3377276417&tx=JR10397LFR&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* A technically trained, hands-on applied analyst with strong statistical foundations and the ability to complete analytical work independently from problem definition through to recommendation.
 * Comfortable translating ambiguous operational questions into structured analytical problems and seeing them through to a finished recommendation.
 * Proficient in Python or R and SQL, with demonstrable experience applying these tools to real-world, incomplete datasets.
 * Experienced in regression, correlation, segmentation, and statistical testing, with the judgement to select an appropriate methodology and interpret its limitations honestly.
 * Rigorous in testing the robustness of findings across markets, channels, dayparts and restaurant cohorts before drawing conclusions.
 * Able to communicate analytical outputs clearly and confidently to non-technical audiences, including in written form and in stakeholder settings.
 * Comfortable working independently under tight timelines and across competing priorities.
 * Curious to learn the details of McDonald's restaurant operations and translate that context into more meaningful analysis.
 * Able to apply analytical thinking within an operational environment, with an appreciation of throughput, capacity, bottlenecks and human factors.
 * Comfortable working in a fast-paced, highly visible global environment and adapting analytical communication for stakeholders from different markets and background., * Bachelor's degree in a quantitative field such as Statistics, Mathematics, Data Science, Economics, Computer Science, Industrial Engineering, or a related discipline. A relevant Master's degree is preferred.
 * Minimum 2 years of hands-on experience conducting statistical analysis and delivering data-driven recommendations in a business environment.
 * Proficiency in Python or R for data manipulation, statistical modelling, and analysis; proficiency in SQL for querying and combining large datasets.
 * Demonstrated experience with regression analysis, correlation analysis, segmentation, and statistical testing; familiarity with experimental design and quasi-experimental methods is a plus.
 * Experience working with large, complex, real-world datasets, including: cleaning, joining, and validating data from multiple sources.
 * Ability to communicate analytical findings clearly in both written and verbal form, including translating technical outputs into operational recommendations for non-specialist audiences.
 * Experience with data visualization tools such as Tableau or Power BI.
 * Microsoft Office proficiency, particularly Excel and PowerPoint.
 * Periodic travel to company locations in the U.S. and internationally for consulting, training or data collection (less than 20% travel commitment).

## Description

This position sits within the Global Operations team working out of the Chicago West Loop headquarters and reports into the Director, Global Operations Strategy and Standards. The role exists to build embedded, hands-on analytical capability within Global Operations, enabling the team to independently turn customer and operational data into robust analysis and actionable recommendations.

Priority areas include identifying the drivers of customer satisfaction, improving how we measure restaurant performance, and evaluating the impact of new operational tools and ways of working. The role will partner with Global Operations, Restaurant Experience and market teams to provide clear, evidence-based recommendations that improve restaurant and customer outcomes.

Position Purpose

 * Independently own the end-to-end analytical process, from translating an ambiguous operational question into an analytical approach, through data acquisition, preparation, analysis, and interpretation, to delivering a clear and defensible recommendation.
 * Serve as the embedded analytics partner for Global Operations, enabling a greater proportion of priority business questions to be answered directly and with rigor.
 * Build and maintain repeatable, well-documented analytical methodologies that can be challenged, extended, and reused across markets and time periods.
 * Translate statistical findings into clear operational recommendations for non-technical stakeholders, identifying both what the data shows and the limitations of what can be concluded., * Lead end-to-end analytical projects: define the analytical approach, obtain and clean relevant datasets, apply appropriate statistical methodology, test robustness, and deliver findings with a clear operational recommendation.
 * Conduct correlation and regression analysis to identify drivers of customer satisfaction and intent to return, extending findings across markets, channels, dayparts, and restaurant cohorts.
 * Analyse operational and customer data, including CSAT, speed of service, throughput, capacity, ArchIQ and Restaurant NEXT measures, to identify performance gaps, improvement opportunities and the potential impact of operational changes.
 * Support the measurement and evaluation of pilots and in-restaurant tests using appropriate test-and-learn and quasi-experimental approaches, assessing whether results are consistent or market-specific.
 * Build reusable, documented analytical code and methodology so that analyses can be repeated, challenged, and extended without starting from scratch.
 * Identify potential performance thresholds, tipping points, and non-linear relationships in operational and customer data, and assess their practical significance.
 * Partner with Global Operations team members to frame business challenges into well-formed analytical questions and communicate findings and limitations in terms relevant to operational decision-making.
 * Collaborate with data engineering and platform teams where required to access datasets, understand data structures, and ensure analytical inputs are valid and reliable.
 * Maintain awareness of relevant methodological developments and apply sound judgement about when standard techniques are sufficient and when more sophisticated approaches are warranted.


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