Job Summary
The Associate Manager – Statistical Operations leads end‑to‑end statistical and analytical operations supporting
RMS and OMNI business planning and decision‑making
. The role ensures high‑quality forecasting,
universe‑level trend tracking, KPI validation, and Store & Sample Maintenance
, along with overall operational rigor using internal, syndicated, and panel data.
This role partners closely with
Analytics, Data Science, Sourcing, Transformation & Enablement, Business, Technology, and People (HR)
teams to deliver reliable statistical outcomes while fostering strong team collaboration and associate development.
Statistical Operations
Own end‑to‑end statistical operations across forecasting and analytics workflows, including data intake, validation, model inputs, output reviews, and downstream hand‑offs.
Ensure
accurate, timely, and SLA‑compliant delivery
of forecasts, validated KPIs, universe metrics, and analytical outputs across RMS and OMNI processes.
Monitor and assess
universe‑level trends
to ensure stability, consistency, and alignment across time periods and data sources.
Perform
KPI validation and data health checks
, ensuring completeness, reasonability, and adherence to defined statistical standards.
Lead
Store & Sample Maintenance processes
, including store and sample additions, removals, corrections, alignment, and ongoing universe maintenance.
Ensure store‑ and sample‑level data integrity through regular validation, reconciliation, and impact assessments on forecasts and KPIs.
Leverage
panel and syndicated data
to support forecasting, universe trend analysis, KPI validation, and statistical measurements.
Support analytical enablement for
new initiatives, pilots, and ongoing programs
requiring statistical support or model updates.
Manage historical and ongoing updates such as
universe restatements, store/sample maintenance updates, trend adjustments, and KPI corrections
, with strong governance and documentation.
Review and validate statistical methodologies, assumptions, and outputs to ensure consistency across datasets, models, stores/samples, and time horizons.
Conduct forecast and KPI performance reviews and drive continuous stability and accuracy improvement initiatives.
Lead and contribute to
cross‑functional analytical projects
aimed at strengthening statistical processes and operational efficiency.
Drive
automation and process optimization
initiatives to reduce manual effort, improve scalability, and enhance data reliability.
Partner with
Analytics, Data Science, and Transformation & Enablement teams
to transition pilots into BAU operations.
Tools & Systems
Hands‑on experience with
R, Python, SQL, and Advanced Excel
.
Familiarity with
enterprise forecasting, planning, or analytics platforms
and data warehouse environments.
Exposure to
automation, scripting, or workflow optimization
initiatives is preferred.
8–10 years of progressive experience
in statistical operations, forecasting, analytics, or decision science roles, preferably within
FMCG / CPG, Retail, or Consumer Analytics
environments.
Proven experience supporting
business planning, demand forecasting, or performance analytics
in large‑scale or matrixed organizations.
Demonstrated exposure to
panel and syndicated data
used for demand forecasting, causal analysis, and trend interpretation.
Experience working with
retailer‑level data
, including promotions, pricing, assortment changes, store coverage, and distribution dynamics.
Prior experience operating in
production / BAU analytical environments
with defined SLAs, governance, and recurring delivery cycles.
Our Benefits
Flexible working environment
Volunteer time off
LinkedIn Learning
Employee-Assistance-Program (EAP)
NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ’s AI Safety Policies and Guiding Principles: https://www.nielseniq.com/global/en/ai-safety-policies.
About NIQ
NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights—delivered with advanced analytics through state-of-the-art platforms—NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world’s population.
For more information, visit NIQ.com
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Our commitment to Diversity, Equity, and Inclusion
At NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence. All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit the
https://nielseniq.com/global/en/news-center/diversity-inclusion
