Analytics and Decision Support Manager

Data Science Reporting and Business Decision Support

• Reports to:
  
  Chief Data and Analytics Officer
• Department: IDEA — Intelligence, Data, Engineering & Analytics
• Team structure:
  Two Team Leads and four Analysts across Data Science and Business Data Support

• The Analytics and Decision Support Manager is the senior hands‑on leader for enterprise analytics, data science, reporting, and business decision support. Reporting to the CDAO, this role manages through two Team Leads, each responsible for two Analysts. Data Science owns analytical methods, predictive model development, experimentation, and model‑performance evidence;
  Business Data Support owns reporting, self‑service analytics, recurring and ad hoc business analysis, and practical support to business users.
• The Manager sets the analytical agenda, ensures that metrics and methods are trustworthy, and turns ambiguous business questions into evidence, recommendations, and reusable decision tools.
• The role must be equally comfortable challenging a model, reviewing a KPI definition, advising an executive, and personally conducting high‑priority analysis.

• Manage, coach, and develop two Team Leads and four Analysts, with clear roles, quality standards, feedback, and accountability.

• Prioritize demand based on business impact, urgency, data readiness, and capacity, with clear ownership and stakeholder communication.

• Own the enterprise analytical agenda and resource plan; hire, assess performance, develop Team Leads, and build succession coverage for critical analytical capabilities. Maintain regular hands‑on involvement in priority delivery.

• Translate business questions into statistical analyses, predictive models, experiments, dashboards, forecasts, scenario models, and recommendations that support specific decisions.

• Ensure deliverables are timely, understandable, and actionable and clearly explain assumptions, limitations, and material changes in results. Supply model evaluation and reproducible performance evidence for BLV and other intelligence products;
  Intelligence Products owns product requirements, acceptance criteria, deployment into use, and outcomes.

• Advise senior leaders on findings, uncertainty, and trade‑offs; challenge business assumptions and analytical methods, and build reusable models and decision tools.

• Establish consistent metric definitions, technical evaluation, documentation, and review practices for reports, models, and analytical outputs. Business owners approve business meaning and retain ownership of policy, decisions, financial assumptions, forecasts, certifications, and recurring business execution.

• Partner with data engineering and business teams to improve data quality, automate recurring work, expand appropriate self‑service, and retire redundant reporting. Provide technical evidence to support any required independent validation and business approval; model development does not constitute independent validation.

• Improve the analytics operating model by reviewing demand, decision usefulness, and recurring quality issues; agree improvements with business owners and track adoption.

• Experience owning an analytics portfolio that combines data science, executive/management reporting, enterprise KPIs, self‑service analytics, forecasting, scenario modeling, and rapid ad‑hoc decision support.
• Experience developing and evaluating predictive models with appropriate train/test design, backtesting, performance metrics, stability or drift analysis, documentation, and clear communication of limitations and intended use.
• Experience defining enterprise…