Directed the development and deployment of custom dashboards and analytical tools, enabling leadership to make informed, real-time decisions based on accurate data insights.Spearheaded the implementation of data-driven decision-making processes, enhancing operational efficiency and improving key performance metrics across the organization.Partnered with teams cross-functionally to deliver comprehensive data solutions aligned with organizational goals, including moving NPS from a baseline of 0 to 47, decreasing the amount of time a home is in design, permitting and construction and improving construction budget alignment.Brought structure and discipline to the process of bringing new technologies into the home building process.Drove continuous improvement in home building technologies.Implemented customer feedback process.

Set a data driven strategy to define SV Academy’s audience segmentation that resulted in a more efficient and cost effective student acquisition.Correlated an applicant’s demographic, cognitive and personality characteristics to successful program outcomes, resulting in tenfold increase in students, while still utilizing the existing sized Admissions Team.Linked applicant characteristics to motivations, resulting in improved admission conversations and decreasing applicant’s time from interview to enrollment by 15%.Predicted monthly student enrollment with 90% accuracy.Worked cross functionally to identify key business statistics to drive change.Implementing data collection processes, building pipelines and creating data visualizations for key business metrics.Improved team performance by changing the analysis of business data, from sporadic data analysis, to weekly cross-functional analysis.Developed analytical and statistical approaches to answer business questions.

Created an entirely new people analytics product that measures and increases diversity at all levels of the organization, exceeding all board-set financial goals for new product development.Set data strategy, including usage of customer data, machine learning opportunities, statistics at scale, and business intelligence.Worked cross functionally to define legal, data, engineering and product requirements for a data warehouse that was built to satisfy product research, marketing insights and business insights that comply with data privacy laws.Wrote business cases at Syndio, resulting in cross functional alignment on a new product.

Managed a PricewaterhouseCoopers (PwC) data science team (12) and product team (2). Drove direction on all aspects of data science work, including model selection, bias handling, data selection, and deployment, increasing team’s time to execute by 75%.Mentored and trained other PwC service teams in best practices for developing software and building AI into models.Launched a product that helped companies upskill their workforce, including creating roadmaps, gathering and prioritizing requirements, translating requirements to engineers and data scientists, and training customer facing teams.Built a TFIDF model to identify the most in-demand skills from job postings.Ensured PwC’s AI is built responsibly, including AI governance, responsible AI testing, and building synthetic data.

Built a new data product (compensation data) into software that had been used by 200 Fortune 1000 customers in its first year, to 2,000+ customers today.Incorporated a natural learning processing model into software resulting in twice the data accuracy and decreasing manual work by 50%.Launched a new product that prices jobs by skill and location, exceeding revenue goal by 10% in its first year.Planned long term vision for data function at PayScale. Developed and executed successful data roadmaps that align with cross-functional software roadmaps. Moved the team from maintaining existing data, to creating new data products while growing the existing dataset.Presented keynotes at multiple User Conferences at PayScale.Hosted conference sessions for customers, describing data collection and cleaning methodology, proprietary compensation algorithm, and job title matching algorithms.Trained sales teams to clearly articulate the product and the data science driving it.Joined sales calls to explain, and objection handle, complex algorithms.Hosted data and product focused webinars.Presented data product strategies to executive leadership and Board of Directors.

Managed a PayScale analytical team (5), including setting vision, directing their work, scoping deliverables, and efficiently running projects from start to finish.Improved and advanced the breadth, depth and quality of the world’s largest real-time compensation database. Increasing the dataset of 65 million data points by 4 million every year and increasing the robustness of the data by 35%.Utilized R to match job titles across customers and build a compensation data set utilizing our customers payroll data.Analyzed new sources of compensation data.Maintained and communicated unique compensation algorithm.

Produce written and graphical analysis of surveys relating to the housing industry. E.g.: http://www.nahbclassic.org/generic.aspx?sectionID=734&genericContentID=221388&channelID=311Produce indices which are watched and monitored by the national media and financial markets.• Monitor and analyze economic data related to housing in order to identify impact on the housing industry.Respond to member, staff, and public requests for economic information.Analyze multiple government datasets to satisfy ad hoc requests and create high-quality original research on housing issues. E.g.: http://www.huduser.org/portal/periodicals/cityscpe/vol14num1/ch5.htmlCreate consumer spending models, e.g., estimating the amount of money spent on residential remodeling by zip code.

Performed statistical sampling and extrapolation on various projects, including PPS (probability proportional to size) sampling, stratified sampling, as well as selecting simple random samples.Assisted with the assessment of a police department’s compliance with a set of guidelines put forth by the Department of Justice. Conducted the review and estimation of Medicare billing practices forecasting, prediction, sample selection, analysis of large volumes of data, statistical and economic modeling.Performed in depth analysis of hiring and recruiting practices within PricewaterhouseCoopers; determined the factors affecting the performance and retention of employees.Extensive experience handling large datasets.