The recent advancement in machine learning and big data technologies allows companies to apply better staffing strategies by taking advantage of historical data. Assigning the right people to the right projects is critical for both the success of each project and the overall profitability of the organization. Most commonly, project staffing is done manually by project managers and is based on staff availability and prior knowledge of an individual’s past performance. This process is time consuming, and the results are often suboptimal. This process can be done much more effectively by taking advantage of historical data and advanced machine learning techniques.
Francesca Lazzeri and Hong Lu share a workforce placement recommendation solution developed for professional services company Baker Tilly Virchow Krause that recommends staff with the best professional profile for new projects. By aligning staff experience with project needs, the solution helps project managers at Baker Tilly perform better and faster staff allocation, with the final goal of improving Baker Tilly’s profit. Based on an offline evaluation, a 4~5% improvement on profit is expected for the projects employing the solution. The solution has been integrated with Baker Tilly’s internal practice management system and will be evaluated in a few pilot teams before being implemented across all teams.
Francesca and Hong offer an overview of the solution and experiment design, which predicts staff composition and computes a “Staff Fitness Score” (rating) for a new project; explore the solution architecture—new projects in Baker Tilly’s database are processed daily by the Azure Machine Learning web service, the results are consumed by project managers in Baker Tilly’s practice management system, and the workforce placement recommendation results are also visualized on a real-time PowerBI dashboard; and explain how they implemented an offline evaluation of the solution using machine learning models (gradient boosting trees) to predict the contribution margin per hour (CMH) of 5,000 randomly sampled projects using the recommended staff and comparing the predicted CMH with actual project CMH to estimate the potential improvement in CMH.
Francesca Lazzeri is a senior machine learning scientist at Microsoft on the cloud advocacy team and an expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries—energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the technology and operations management unit. At Harvard, she worked on multiple patent, publication and social network data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world. She’s a data science mentor for PhD and postdoc students at the Massachusetts Institute of Technology and speaker at academic and industry conferences—where she shares her knowledge and passion for AI, machine learning, and coding.
Hong Lu is a data scientist at Microsoft. Hong is passionate about innovations in big data technologies and application of advanced analytics to real-world problems. During her time at Microsoft, Hong has built end-to-end data science solutions for customers in energy, retail, and education sectors. Previously, she worked on optimizing advertising platforms in the video advertising industry. Hong holds a PhD in biomedical engineering from Case Western Reserve University, where her research focused on machine learning-based medical image analysis.
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