Head of Data Science

Head of Data Science - Asset Risk in Dubai, United Arab Emirates is a senior financial services leadership opportunity focused on portfolio analytics, asset valuation, forecasting, artificial intelligence, and financial risk management. Al-Futtaim is hiring an experienced leader to transform complex market, vehicle, and portfolio data into clear decisions affecting pricing, leasing, provisioning, capital allocation, profitability, and balance sheet performance.

Position: Head of Data Science - Asset Risk

Location: Dubai, United Arab Emirates

Industry: Financial Services

Function: Risk Management

Experience: Minimum 10 years

Job Type: Full-time

• Own end-to-end analytics, forecasting, valuation, and risk intelligence for automotive and financial services portfolios.
• Convert market data, statistical analysis, artificial intelligence, and portfolio information into financially measurable actions.
• Develop analytical recommendations that improve pricing, leasing, profitability, return on assets, and capital efficiency.
• Maintain authoritative market indices for vehicle brands, models, powertrains, customer segments, and asset categories.
• Build and govern current and forward price curves used for residual value assessment and portfolio planning.
• Produce forward-looking scenarios covering market movements, technology shifts, demand changes, and economic conditions.
• Define and enforce an enterprise Asset Risk Framework across relevant portfolios.
• Assess market risk, residual value risk, concentration exposure, price volatility, and electric vehicle transition risk.
• Establish risk thresholds, escalation criteria, monitoring standards, and governance requirements.
• Provide risk-based analytical inputs for IFRS9, expected credit loss, impairment, provisions, and capital planning.
• Support finance and risk teams with scenario analysis, sensitivity testing, and forward-looking portfolio assumptions.
• Ensure valuation, forecasting, and risk models are transparent, reproducible, auditable, and regulator‑ready.
• Maintain clear model documentation covering data sources, assumptions, methodology, validation, controls, and limitations.
• Act as the recognized source for portfolio valuation, forward price risk, stress outcomes, and financial impact analysis.
• Chair Residual Value and Risk Committee meetings and guide stakeholders toward clear commercial decisions.
• Translate advanced statistical findings into concise recommendations for the CFO, CRO, executive management, and board members.
• Influence vehicle pricing strategy, leasing terms, deposit structures, contract duration, and residual value assumptions.
• Support decisions related to portfolio profitability, risk appetite, provisioning, and capital allocation.
• Provide analytical insight for OEM discussions, new vehicle launches, portfolio expansion, and product design.
• Own the AI‑enabled asset intelligence platform supporting data pipelines, valuation engines, forecasting, and risk models.
• Ensure data architecture and analytical workflows provide reliable, timely, and consistent portfolio intelligence.
• Drive improvements in data quality, model performance, scenario capability, reporting automation, and decision speed.
• Lead senior Data Scientists, analytics professionals, and specialist modelling resources.
• Set team priorities, technical standards, development objectives, and quality expectations.
• Review model output and challenge assumptions before findings are presented to senior decision‑makers.
• Coordinate with finance, treasury, credit risk, leasing, automotive, technology, accounting, and executive stakeholders.
• Monitor external developments affecting vehicle values, including electric vehicle adoption, supply levels, interest rates, and consumer demand.
• Evaluate the financial consequences of market disruption and recommend timely portfolio actions.
• Establish performance measures linking analytical work to profit‑and‑loss, balance sheet, and return‑on‑asset outcomes.
• Promote disciplined model governance, responsible AI use, and evidence‑based executive decision‑making.

• Minimum 10 years of experience in statistics, data science, asset risk, pricing, forecasting, financial modelling, or portfolio analytics.
• Strong professional background in automotive, asset finance, banking, insurance, leasing, or large portfolio management.
• Demonstrated ownership of analytical models with direct impact on profit‑and‑loss, provisions, pricing, or balance sheet outcomes.
• Advanced knowledge of data science, statistical modelling, predictive analytics, and scenario development.
• Strong understanding of asset valuation, residual v