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Decision Analytics Manager – Senior Manager, Decision, Modeling, Economics
EY. Lead advanced analytics engagements for government and public sector clients across the GCC and selected sectors including infrastructure, energy, investment, education and transactions .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading advanced analytics engagements, including predictive modeling, optimization, and simulation for government and public sector clients. Proficient in translating complex analytical findings into actionable insights and decision tools while managing multidisciplinary teams and client relationships.
Highest-signal resume keywords
Advanced Analytics LeadershipPredictive ModelingPython or R, SQLModel Validation and TestingClient-Facing Consulting Skills
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Predictive ModelingOptimizationSimulationScenario AnalysisSensitivity AnalysisModel ValidationTestingVersion ControlReproducibilityDecision Tools
Soft Skills
Strong English CommunicationClient Relationship ManagementCoaching and Mentoring
Industry Keywords
Government SectorPublic SectorData ScienceOperational ResearchQuantitative ConsultingAI GovernanceData PrivacyCapital AllocationValue CreationInvestment Cases
Tech Stack
Tools & technologiesPythonSQL
About the role
Key responsibilities & impact- Lead advanced analytics engagements for government and public sector clients across the GCC and selected sectors including infrastructure, energy, investment, education and transactions
- Lead analytics workstreams from problem definition and data assessment through modeling, validation and implementation
- Develop predictive, forecasting, optimization and simulation models
- Apply analytics to transaction and corporate-finance questions, including customer performance, concentration, recurring revenue, investment cases, value creation and capital allocation
- Select methods, technology and development approaches for engagements
- Direct data scientists, engineers, analysts, economists and modelers, including code and model reviews
- Set standards for testing, documentation, version control, reproducibility and model governance
- Identify data, methodological, bias, privacy and implementation risks and define mitigations
- Guide transition from prototypes to repeatable tools, production solutions or client-owned processes
- Implement AI-enabled and agentic workflows where appropriate, with human review and approved data-governance controls
- Lead client relationships, facilitate workshops and communicate methods, assumptions and limitations
- Translate findings into executive-ready insights, recommendations, presentations, decision tools and dashboards
- Define adoption measures, performance indicators and benefits-tracking approaches
- Manage team responsibilities, technical reviews, risks, budgets and timelines
- Coach junior colleagues through technical guidance, feedback and on-the-job learning
- Contribute to proposals, solution design, scoping, estimates and client presentations
- Identify follow-on opportunities and develop reusable methods, code, model templates and analytical accelerators
- Convert project solutions into repeatable offerings and recurring analytical services
Requirements
What you’ll need- Typically 5-10 years of relevant experience in data science, operational research, decision science, advanced analytics or quantitative consulting
- Degree or equivalent expertise in a quantitative discipline such as statistics, mathematics, operational research, data science, computer science, engineering or economics
- Demonstrated experience leading advanced analytics, optimization or simulation workstreams, ideally for government and public sector clients
- Experience validating models and communicating accuracy, uncertainty, assumptions and limitations
- Experience reviewing technical work and translating outputs into stakeholder-ready decisions
- Predictive modeling, forecasting and machine learning
- Optimization and simulation
- Scenario and sensitivity analysis
- Python or R, SQL and complex datasets
- Model validation, testing, version control and reproducibility
- Decision tools and dashboards
- Strong English communication and client-facing consulting skills
- Ability to lead multidisciplinary teams and complex analytical workstreams with limited senior oversight
- Commercial awareness and interest in building client relationships and identifying follow-on opportunities
- Commitment to responsible use of data, algorithms and AI
Benefits
Comp & perks- Flexible environment
- Diverse and inclusive culture
- Future-focused skills development
- World-class experiences
- Disability-related recruitment adjustments or accommodations