Senior Data Operations Analyst - Statistics

Role Summary
The Senior Statistical Operations Analyst is responsible for executing statistical operations processes with accuracy, quality, and timeliness. The role supports the delivery of client and market reporting by performing production activities, monitoring quality, resolving operational issues, and collaborating with cross-functional teams. The individual serves as a key contributor in day-to-day operations, ensuring adherence to established processes while building strong stakeholder relationships and supporting continuous improvement initiatives.
Key Responsibilities
Production & Quality Execution:
Execute assigned production and reporting processes accurately and within defined timelines.
Ensure high-quality data delivery through adherence to standard operating procedures and quality controls.
Perform validation checks, investigate data discrepancies, and support timely resolution of production issues.
Monitor production milestones and proactively escalate risks that may impact quality or delivery timelines.
Contribute to achieving operational KPIs related to timeliness, quality, and productivity.
Support recurring production cycles while identifying opportunities to improve efficiency and reduce manual effort.
Process Compliance & Continuous Improvement:
Follow established methodologies, guidelines, and governance standards.
Maintain process documentation and support updates when process changes occur.
Participate in process improvement and automation initiatives led by senior team members.
Apply best practices and recommend practical enhancements to improve workflow effectiveness.
Ensure compliance with data quality and operational standards.
Issue Resolution & Problem Solving:
Identify, investigate, and resolve routine operational and data-related issues.
Conduct preliminary root cause analysis and collaborate with senior team members on complex issues.
Escalate critical risks and exceptions appropriately while ensuring timely follow-up.
Track issue resolution progress and communicate updates to relevant stakeholders.
Stakeholder & Client Support:
Coordinate with Client Operations, Data Operations, Global Operations, and other internal teams to ensure smooth execution of deliverables.
Respond to stakeholder queries in a professional and timely manner.
Support client-related requests and data investigations under guidance from senior team members.
Maintain effective communication with onshore and offshore stakeholders regarding project status, risks, and delivery updates.
Build collaborative working relationships across functions and geographies.
Learning & Capability Development:
Develop expertise in NIQ methodologies, systems, tools, and operational processes.
Continuously enhance analytical, technical, and business knowledge relevant to the role.
Actively participate in training sessions and knowledge-sharing activities.
Support onboarding and knowledge transfer activities for new team members when required.
Qualifications
Work Experience
2+ years of experience in Market Research, Statistical Operations, Data Analytics, Business Operations, or a related field.
Experience working with large datasets, reporting processes, or operational analytics.
Exposure to client-facing interactions or stakeholder coordination is preferred.
Experience in a fast-paced, delivery-oriented environment is advantageous.
Familiarity with NIQ tools and methodologies is preferred.
Education
Bachelor’s or Master’s (preferred) degree in Statistics, Mathematics, Data Science, Computer Science, Economics, or a related quantitative field.
Core Competencies
Execution Excellence: Demonstrates strong attention to detail and consistently delivers accurate work within agreed timelines.
Accountability: Takes ownership of assigned deliverables and proactively manages risks and dependencies.
Analytical Thinking: Ability to investigate issues, identify patterns, and support data-driven problem resolution.
Continuous Improvement: Actively seeks opportunities to improve quality, efficiency, and operational effectiveness.
Collaboration: Ability to work cross-functionally with Client Ops, Data Ops, Data Science and global teams to achieve business objectives.
Technical Skills Preferred
Understanding of basic statistical concepts and data management principles.
Familiarity with data validation, quality assurance and data analysis techniques.
Knowledge of data analysis tools (e.g., SQL, Python, R) and business intelligence tools is preferred.

Employment type
Full-time

Work arrangement
No