Sonali Saxena, M.S.

About Sonali Saxena

Ms. Sonali Saxena is a Research Associate for Analytic Focus, a national statistics, finance and economics consulting organization. Ms. Saxena has 5 years of experience in conducting statistical and demographic analysis and econometric models for clients. Ms. Saxena specializes in the use of quantitative statistical methods to support cash flow modeling and “but for” losses that result from the introduction of toxic conditions to financial transactions (for example, detecting fraud).

Ms. Saxena also has experience with healthcare claims data to support performance improvement by conducting inter-regional analysis of clinical quality and utilization, using both industry standard metrics, such as HEDIS, and locally developed metrics in order to identify complex associations to better understand the drivers of health outcomes or costs as also to improve decision-making, enhance patient care and outcomes, optimize resource allocation and manage risk through all the analytical support.

Originally from New Delhi, India and came to the United States to pursue a graduate degree in Statistics with emphasis on Biostatistics. Her mathematics expertise was applied at Washington Mutual Bank as a Vendor Relations Analyst and later her statistical modeling skills were called for by Kaiser Permanete where she served as a Healthcare Data Analyst. While at Penn State University and later at Deanza College she taught Probability Theory and conducted thorough statistical analysis of student survey data to help understand effect of community engagement on Students’ GPA. Ms. Saxena built a robust multiple regression model, which identified significant factors that affect student GPA. Ms Saxena holds a BA and MA in Pure Mathematics from St. Stephen’s College, Delhi University, India and obtained a Masters in Statistics from CSU East Bay, Hayward CA.

Ms. Saxena's Resumé 

Expertise

BASE SAS 9 Certified

Factor Analysis

Predictive Modeling

Bootstrapping

Forecasting

Quantitative Research

Cluster Analysis

Logistic and Linear Regression

R

Discriminant Analysis

Statistics

Time Series