Ahmed Hannan

Commercial Finance Team:

· Implemented add-ons for in-store customers for MIAM & guac/sour cream, resulting in a $800k annual sales boost across 180 stores.

· Generated actionable insights and recommendations through data analysis to support strategic decision-making processes.

· Liaised with Marketing team to develop mertics for decision making on personlaised orders on Apps/Delivery Orders

· Part of the weekly P&L meetings and helping the culinary coaches optimize efficiencies and increase sales

· Featured Partner of the Microsoft AI tour 2024 from the GYG team.

· Developed and maintained comprehensive dashboards and reports for key performance indicators (KPIs) such as sales, inventory, customer behaviors, and market trends.

· Collaborated with the tech team to migrate on-premises services to the cloud (Fabric), negotiating with external consultants on migration processes and pricing.

· Analyzed sales data, labor patterns, and trends to accurately forecast guest demand and optimize crew levels.

· Collaborated with leadership, commercial insights, operations, supply chain, and marketing teams to develop data-driven strategies.

· Worked closely with marketing to implement data-driven approaches for measuring the impact of marketing efforts on sales conversion rates, customer acquisition, and retention.

· Foster a data-driven culture within the organization, promoting the use of business intelligence tools and techniques

Range Optimization:

· The AA Range Optimizer (RO) tool has been used by SMKTS for 2+ years driving 200+ range reviews a year and has delivered significant measurable benefits to sales + margin and a reduction in SSC effort

· Sales: +1.3% sales uplift ($30m per annum) for every additional 10% of compliance with RO recommendations by liaising with different business partners such as Category Managers/ Business Category Managers/ GM

· 75% time saved for the category managers for deciding them the items to be placed for the next 12 weeks.

· Managed a team of 6 Data Scientists/ Senior Data Scientist/ Data Engineers/ RShiny developers

· Conducted analysis on R using Azure platform and ETL process conducted via data bricks query

· Created Customer Decision Tree algorithm to segment products on the basis of Financial & Marketing metrics. Models implemented are:

· K Means Clustering

· Hierarchical clustering

· Worked with a team of talents to Implement the Optimization Model used in Range Optimizer by Integer Linear Programming

· Led the implementation of Azure data and analytics platform and successfully managed deploying self-service apps using Rsconnect
· Successfully deployed Disc -> Prod -> Ops conducting rigorous systems & UAT testing.

· Created scope statements, cost estimates, budget, risk for relevant stakeholders using JIRA/confluence with the help with Delivery Lead.

· Negotiated with the stakeholders on cost estimates and delivering quality product for the business

· Created Sprint tasks and PI planning with liaising with Data Science Delivery Lead and team members

· Reviewed code from team members using bitbucket

· Responsible for strategic planning of analytical roadmap and technical vision for the team

· Responsible for accurate category management for Digital Team (App/Delivery orders) to measure last mile business metrics and how RO would affect the changes such as :

• Financial Metrics
  Average Check for each delivery order, Deliver Cost per order
  Delivery Cost as a % of Sales, Vehicle Utilization Rate,
  Vehicle cost per KM, etc
• Operational Metrics
  On Time Delivery Performance, Delivery Success rate, Customer Satisfaction Score,Average Delivery Distance, etc

· Developed and mentored a team of data scientists and guide them in their professional development

· Liaised with personalization team for the deleted items and it's substitute items to be presented on the app

Tailored Ends:

· Currently, ends are mostly generated at the National level- there are typically 60-80 standard ends generated on a weekly basis from Ends 1-20.

· Tailoring the ends using Ranked cut algorithm model and handcrafting solution and increasing the sales uplift of the control stores by 17% PW ($10k PW per store) estimating an uplift of $40m per annum

· Created different metrics which can be used in the model

· Negotiation with project managers and relevant stakeholders on the items to be placed on ends

· Product Manager of an App called Range Optimizer (build on RShiny interface)which recommends which are the items we should keep/delete in the store on the basis of Sales PSPW, Customer Loyalty, substitutability, sales transfer, waste markdowns.

· RO app also provides an optimization on the facings of an item looking at HFACINGS, VFACINGS, DFACINGS, and relevant coordinates of an item in a store.

· Built relevant metrics such as Associated basket (%/growth/index), BRAGG status, consecutive purchases brand, AWOP, Residual Basket which are used on the model outputs.

· Model being built is a complex mathematical model which is a combination of integer linear programming (ILM) build by mathematicians.

· Liaised with Category Managers/Business Category Managers on how such items on a particular store had a recommendation of delete on the basis of metrics provided to them.

· Validated model outputs and suggesting improvements on the model.

· EDA/ algorithm done on Rconnect using azure platform.

· Created summary outputs and showing different comparison on current assortment vs recommended assortment

· Created interactive BI tools for C level executives on how we (Smarter Forecast) is predicting compared to other forecasting tools created by IBM (RDF SYS)

· Liaised with demand planner team on translating the model outputs into business terms and explaining them based on different features such as on promo, on end, on catalogue

· Recommended on the next best items to order if an item have an ongoing item availability issue

· Deep dived for categories which had been performing worse than RDF SYS and improving the model with addition of features which could help the model RMSE.

· Time series model (ARIMA/SARIMA), Facebook Prophet, XGBoost are implemented as our prime models for prediction of 16 weeks forecast.

· Translated the model into simple terms to the relevant stakeholders such as demand planner, category managers, digital team, labour roster managers, etc for both instore and app performance.

• Led an agile team of data scientists delivering business intelligence (BI) solutions by transforming data into insights that shape the strategy for the business division.
• -A business liaison to understand the division's data needs and develop a delivery roadmap to design scalable Bl solutions, and optimize self-service capabilities.
• Led an end-to-end delivery of the critical operational BI dashboards like contacts, sales & pipeline, & TTD, using one source of hierarchy.
• Steered the implementation of NextGen advanced analytics platforms like MS platform - Power BI & Power apps and helped to increase the adoption through effective change management & business engagement. - A winner of a "Team Award" for successfully leading an end-to-end data migration project with 27 remote team members ahead of time and under the agreed $ budget.
• A vital member of the Analytics & Insights Centre of Excellence (CoE) team collaborating with the cross-divisional heads about the analytics pipeline and the analytics delivery lifecycle. - A trusted advisor to instill data-driven culture for decision-making and deliver presentations to C-suite executives. - Assist the head of finance in simplifying the financial

• Managed the Data and Analytics group within the Business Banking division of Westpac
• Part of COVID Project team which won the Finance Professional Awards for data analytics
• Co-ordination with the Group Data team to design, prioritize and build new metrics into Data systems
• Stakeholder engagement to ensure a strong working relationship is maintained with Group Data, Heads of, Business GMs/ SGMs/ RGMs, Operations Managers and frontline staff.
• Product Manager for the Business Banking Platform called OneView, which shows the bankers performance.
• Project Management, raising tickets in JIRA and resolving the issues.
• Predicted customers subscription for Fixed Term Deposits. Models implemented are: Logistic Regression, Decision Trees, Random Forest, Ada Boosting, XG Boost

• Liaised with an offshore team (Jaipur India) which reconciles the Fraud Suspense Account and made sure the actual Fraud Losses matches the reports created for stakeholders
• Involved in the annual Business Planning process/ exposure to frontline.
• Liaised with regulators to create reports for them for different losses in Fraud types; this requires to know the reports other banking department within the group provides as for Data Matching
• Supported the different Portfolio Performance & Insights cost center owners managing the momentum and performance of their portfolios, such as business banking, home loan, etc.
• Project owner for the Merchant Fraud Data Platform which creates a platform for the regulators to monitor the merchant accounts/terminals which were used by the Fraudsters. This was mostly to promote 3D secure for Merchants.
• Collated Data from different data sources by the help of Data Architecture and created Risk Appetite Dashboard • Forecasting Loss Amount for different Risk Families and which planned budget for different cost centers along the business.
• Raised Provisions by liaising with the Product Owners for the High Fraud Amounts as those had been escalated to Group Investigations and would take time for the customer to be reimbursed.
• Used SAS, SQL (Teradata) and Excel to analyze fraud trends and transactional Data
• Took decisions about which cost center the Fraud amount should be allocated in. This involved lots of reading of business compliances so there’s no conflict between two different cost centers.
• Prepared fraud performance reporting that accurately reflects the impact of fraud on the Westpac Group both financial and non-financial side.
• Maintained the integrity of data contained in fraud case management systems and the fraud general ledger by performing reconciliations.
• Prepared financial and statistical reports on a daily, weekly and monthly basis to provide the necessary information to management to effectively manage fraud.
• Automated reporting, including making changes due to the implementation of new systems and products.
• Responded in a timely manner to fraud performance related ad-hoc requests.

• Carried out data preparation, manipulation and exploratory analysis on both client’s and internal data using extensive SQL Server Management Studio, DB2, R and Python
• Responsible for the assessment, development and/or recalibration of IRB credit risk models (PD, LGD, EAD) in the context of Basel III
• Performed data sourcing, analysis and quality checks to ensure data is suitable and robust for modelling purposes
• Ensured that all modelling processes, decisions and outcomes are appropriately documented
• Data and insights visualization using tools like Microsoft Excel and Tableau
• Developed analytical, reporting and benchmarking capabilities to manage the ‘end to end’ performance of the mortgage portfolio
• Responded to senior leaders, regulator and other industry participants enquiries into the mortgage portfolio with timely insights
• In conjunction with Product Management team, deliver insights that enable the development of value propositions to more effectively acquire, retain and ultimately grow priority segments
• Design and development of Data Quality reports for consumer and commercial entities
• Implemented methods to match customers across multiple datasets
• Built framework to automate data integrity checks and alerts
• Leveraged Veda’s database as well as third party data sources to develop predictive, descriptive and consultative insights and analytical pieces
• Deep engagement with sales and solution consultants for design and development of presales proof of concepts tailored specifically to client’s areas of interest and needs
• Large Data washes, default conversions/deletions, address cleansing for debt collecting agencies using extensive SAS and uploading into Data Enrichment System (DES)
• Maintained existing relationships by working directly with clients, addressing ad-hoc requests relating to current analytical solutions and initial scoping and requirements- gathering of potential insights projects
• Prepared recommendations to steering committee of company general managers to guide overall strategy of the Veda’s Database
• Built Fraud Reports for leading banks in Australia
• Worked with teams to implement Cambrian Hadoop data platform for analytical efficiencies
• Trained 3 fresh graduates in the team.

• Generated regular reports and dashboards to track key performance indicators (KPIs) and metrics.
• Prepared visualizations, charts, and graphs to present analytical findings in a clear and concise manner.
• Assisted in creating ad-hoc reports and presentations for internal stakeholders and management.
• Assisted in collecting, cleaning, and organizing data from various sources.
• Performed exploratory data analysis (EDA) to identify patterns, trends, and anomalies.
• Used statistical methods and tools to analyze data sets and extract valuable insights.
• Collaborated with senior analysts to develop and refine analytical models and algorithms.