San Francisco, California, United States
A Finance Manager with technical skills and 14 years of experience using data to make decisions. I’ve supported Sales organizations with revenue analytics, forecasting, planning, and go-to-market decision support. I’ve worked in both consumer (Ads) and Enterprise (Cloud, SaaS) businesses. My strengths as a manager are being able to cater to different working styles and personalities, and empowering the team to take ownership and work autonomously.
Lead all activities related to revenue (forecasting, planning, reporting) & the SMB business: * Coordinate long range (5-year) revenue planning, using driver based model linking headcount investments, productivity, and bookings to revenues * Drive global Sales revenue quota setting model and process; present to VP of Sales and VP of Sales Ops; partner with central/regional Sales Ops, regional Sales Finance, BI, Comp teams * Lead monthly cross-product forecasting process to update Cloud CFO on latest outlook vs annual plan; own the analytics and insights on GCP revenue trends * Drive engagement with Data Engineering on consolidated Cloud revenue data warehouse * Finance lead for Cloud SMB business; partner with SMB Sales Senior Director to establish and track efficiency of programmatic sales model; drive Sales-Marketing alignment * Involved in Comp design, to overhaul the incentivization schemes for all customer teams
Managed the SMB Ads Same Store Programs ($40B annual revenue); led team of 4 analysts: * Led annual planning and resource allocation for Mid Market, Agency, and Scaled (outsourced) sales; anchored top down planning on work driver growth and sales productivity * Spearheaded massive multi-year data infrastructure project (a CFO-level OKR) to re-engineer the Ads data warehouse to integrate data on SMB Programs operations; lead from project conception to requirements to design, and through the user acceptance process * Conducted a rigorous benchmarking analysis of mid market vs large customer segment performance; reviewed by Google’s CBO, and led to shift in channel allocation strategy * Deeply involved in numerous strategic projects to design and optimize go-to-market strategies such as segmentation, account coverage ratios, book sizes, sales roles, scaled operations * Conducted ~400 interviews; led the MBA intern hiring process for Ads Finance
Owned revenue forecasting and revenue quota setting for SMB Ad Sales teams, comprising $8B of quarterly revenue across > 2,000 HC: * Built time series forecasting model using SQL and R; model used to set global revenue quotas * Led quarterly reviews with all Sales VPs, Directors, Managers to analyze business trends and historical performance vs quota; hosted Q&As w/hundreds of sales reps on quota modeling * Created ROI framework to measure efficacy of Sales Programs, combining test vs control holdback experiments and propensity modeling to measure incrementality of Sales’ impact; used for resource allocation decisions, evaluation pilots, and tracking profitability
* Promoted to team lead for building the models and algorithms that drive Fannie Mae's credit loss mitigation decisions on make-wholes/repurchases, forensic reviews, and loan quality control * Manage two analysts on the team. Was instrumental in recruiting, interviewing, hiring, and training the new analysts and economists in my department * Regularly interface with senior management to guide key business decisions relating to the team’s research * Work collaboratively with other loss mitigation research teams, such as servicing, property valuation & disposition, and anti-fraud
* Promoted from Research Analyst in March 09 * Developed models and algorithms to guide business decisions on credit loss mitigation and loan quality assurance * Created a logistic regression model to predict the likelihood of a defaulted loan having underwriting or valuation fraud at origination. This model was placed into production and ran daily to select high-risk defaulted loans for review, leading to the collection of repurchase and make-whole proceeds from lenders totaling billions of dollars * Developed a model to estimate the total future liability of lenders on the representations and warranties made to Fannie Mae at loan acquisition. These estimates were the basis of negotiations by senior management with executives at various counterparties, including lenders and the FDIC. These discussions resulted in the posting of collateral by counterparties, thus insuring Fannie Mae against future losses * Created and generated reports to help management monitor business operations * Worked around technical issues brought about by legacy systems, as well as discrepancies between the variety of internal systems and databases within the company * Communicated technical results, such as presentations of regression models, to non-technical audiences * Analysis was performed using SAS and SQL, working with large and complex data sets of loan delinquency, default, and severity
* Responsible for the production of and analytics relating to the Servicer Scorecard, a program to monitor and incent the improvement of Fannie Mae’s servicers, resulting in $18 million of savings to Fannie Mae from servicing performance improvements * Communicated regular updates on Scorecard performance to senior management at servicers, as well as portfolio managers at Fannie Mae responsible for each servicer’s performance * Produced the Scorecard using SAS and SQL. The output of SAS and SQL programs was loaded by a production team into a Business Intelligence / Business Objects (BIBO) environment for distribution to users. Managed the production process by working closely with the technology team