Senior Manager, Social Statistics

OVERVIEW

Position

Senior Manager Social Statistics

Job Code

Reports to

Executive Director Data and Statistics

Direct Reports

N/A

Division/Section

Data and Statistics

Department

Economics, Data and Statistics

Sector

Economics, Data and Statistics

Job Family

Role Purpose

The Economics, Data and Statistics Sector is tasked to build a brand-new Statistics Office for NEOM in a greenfield environment in order to produce robust data and statistics about people, the economy and different sectors of interest. Building such a Statistics Office in a fully digitalized context offers unique opportunities to gain deep real-time statistical insights and take the production of official statistics to a level way beyond what is possible in legacy environments. A Senior Manager for Social Statistics is required to lead the work on Social Statistics. She/he will be building a team of outstanding statistics and data science experts to produce the set of required high-quality official statistics in very little time. The Senior Manager for Social Statistics will activate data already available in NEOM for the production of official Social Statistics, engaging with all stakeholders in order to build automated data pipelines and create a harmonized data ecosystem. Additionally, she/he will explore new innovative data sources and methodologies for the production of Social Statistics and contribute to turn NEOM’s Statistics Office into a unique information center, making it a role model in the global statistical community.

KEY ACCOUNTABILITIES & ACTIVITIES

Statistics Production

• Leadership and management responsibility for the production of Social Statistics, covering:
  • Population and Demographic Statistics
  • Labour Market Statistics
  • Education, Culture and Entertainment Statistics
  • Social Statistics and Living Conditions
  • Gender and Diversity Statistics
  • Justice and Safety Statistics
  • Health Statistics
  • And any new field of Social Statistics that might emerge as a priority for NEOM in the future.
• Establish a team of highly qualified social statisticians and data scientists with respective experience and domain knowledge in their field.
• Guide the Social Statistics team to adhere to international standards, classifications and definitions when producing official statistics for NEOM.
• Implement a rigorous process management of the production of official statistics, following the GSBPM approach.
• Ensure the high quality of statistics produced in compliance with internationally agreed quality criteria of official statistics.
• Make sure that the portfolio of Social Statistics meets the highest standards of best international benchmark statistics offices in terms of topics covered as well as depth of statistical insights, following the priorities in NEOM.

Statistical Design, processing and Analysis

• Design statistical products in Social Statistics according to international standards and best practices, leveraging on the potential of the high degree of digitalization in NEOM’s cognitive city approach.
• Collaborate with researchers, analysts, and stakeholders to formulate research questions and hypotheses.
• Guide the respective Social Statistics teams working on individual statistical products to follow the GSBPM approach, striving for the highest quality and accuracy of data and statistical outputs.
• Apply modern statistical methodologies, techniques and tools when processing the data.
• Conduct statistical analyses in the respective fields of Social Statistics sets using advanced quantitative methods, including AI/ML.
• Apply appropriate statistical techniques to explore relationships, patterns, and trends in social phenomena.

Statistical Quality and Reporting

• Maintain statistical quality management systems and processes to ensure compliance with relevant standards, regulations, and best practices within the area of Social Statistics.
• Develop and monitor key performance indicators (KPIs) and metrics to assess the effectiveness of quality management initiatives and identify areas for improvement.
• Establish data quality standards, protocols, and procedures to ensure the accuracy, completeness, and consistency of statistical data.
• Implement data validation, verification, and auditing processes to identify and resolve data quality issues in a timely manner.