Head of Advanced Analytics will build and lead a centralized analytics capability that unlocks value from the company's diverse data ecosystem across markets and brands. The successful candidate will build and lead a team of data scientists, analysts, and data engineers, and partner closely with leaders across marketing, store operations, supply chain, finance, HR, Legal and digital commerce to drive measurable impact across the business.
This role combines advanced analytics leadership, AI transformation, stakeholder influence, and team development to drive measurable commercial and operational outcomes across Americana's restaurant portfolio.
The role is expected to establish an enterprise level data architecture integrating data across multiple reporting systems, including customer data, restaurant operations, last mile delivery platforms, financial performance, and employee data for advanced analytics use cases and data backed decision making.
Key Responsibilities:
- Build and Lead the Advanced Analytics Function
Establish and lead Americana's Advanced Analytics Center of Excellence. Define the analytics vision, roadmap, and operating model. Recruit, develop, and lead a high-performing team including:
- Data Scientists
- Advanced Analysts
- Data Engineers
- Visualization specialists
Implement best practices in AI transformation, machine learning, and data-driven decision making
2.Develop analytical models to drive business efficiency and transformation
Create advanced analytics models to deliver high-impact projects across multiple domains, including:
- Customer & Growth Analytics
- (customer segmentation, cohort analysis, churn, frequency, LTV, acquisition effectiveness)
- Marketing Analytics & ROI Measurement
- (campaign effectiveness, attribution, media ROI, performance tracking)
- Personalization and Recommendation Engines
(menu layout and customization, customer journey personalization, CRM personalization etc.)
- Sales Forecasting & Commercial Planning
- (demand forecasting, store-level planning, budgeting and outlook support)
- Pricing & Menu Analytics
- (price elasticity, menu optimization, mix improvement, margin diagnostics, basket analysis)
- Promotions & Campaign Analytics
- (promo affinity modelling, uplift measurement, cannibalization analysis, offer effectiveness)
- Labor, home delivery & transport optimization and scheduling models
(delivery time prediction, logistics efficiency, labor transport scheduling, labor productivity)
- Store & Operations Analytics
- (store performance diagnostics, productivity, cost optimization, efficiency levers)
- Workforce Analytics
- (employee performance analytics, attrition models)
Ensure analytics outputs are decision-oriented, commercially relevant, and actionable.
2. Integrate Data Across Enterprise
Oversee the creation of robust, scalable, and high-quality data foundations to enable analytics.
- Develop frameworks to integrate data across multiple systems customer data platform, restaurant POS and operating systems, delivery logistics platforms, enterprise financial and sales reporting systems, HR and employee systems
- Own data sanity, metric definitions, and consistency across systems
- Ensure alignment and synchronization between source systems, data layers, and analytical outputs
- Manage the end-to-end customer data stack, ensuring usability for analytics and business teams
3. Embed Analytics into Decision Making
Drive analytics and AI initiatives from problem definition to impact delivery:
- Partner with business stakeholders to identify high-value analytics and AI use cases
- Lead experimentation frameworks (A/B testing). Promote a data-driven culture across markets and functions
- Lead project scoping, prioritization, and delivery governance
- Ensure models, insights, and tools are explainable, trusted, and adopted
- Track and articulate business impact and value realization
4. Establish Analytics Governance & Best Practices
Act as a trusted analytics partner to senior stakeholders across Marketing, Commercial, Operations, Finance, and Digital:
- Define standards for Data modeling, Machine learning deployment, Experimentation, Analytics documentation
- Ensure compliance with data privacy and governance standards
- Partner with technology, data engineering, and IT teams to ensure sustainable data access and governance
5. Team Leadership & Capability Building
Build and lead a high-performing analytics team:
- Manage, coach, and develop analytics talent across experience levels
- Foster a culture of curiosity, ownership, and continuous improvement
- Build future-ready capabilities across advanced analytics, forecasting, and AI
Education:
- Bachelor's or Master's Degree in Engineering in Computer Science / Mathematics/ Statistics / Economics
Experience:
- 10+ years of experience in analytics, data science, or related fields with exposure to stakeholder engagement and project management.
Technical Skills:
- Strong foundation in statistics, predictive modeling, and machine learning.
- Strong exposure to F&B, QSR, retail, or consumer businesses preferred
- Hands-on experience with Python, SQL, and data visualization tools (Tableau / Power BI).
- Experience with customer data platforms, experimentation frameworks, customer analytics techniques (LTV, cohort analysis, segmentation, churn modeling), marketing & pricing analytics frameworks and forecasting techniques.
Other Skills:
- Strong business acumen with the ability to connect analytics to commercial outcomes.
- Excellent communication skills, able to present complex insights to non-technical audiences.
- Structured problem-solving mindset with strong prioritization skills.
- Strong grounding in statistics, forecasting, and applied analytics
- Excellent stakeholder management and communication skills
