Moved to ML & data science at Microsoft. Working on ML/Stats methods to understand and improve customer engagement in using Skype. Loving it! Before Microsoft: I worked on Big data in genetics & Personalized Medicine. I like to start with the big picture of a problem, translate the problem into statistical framework, visualize and analyze the data to finally explain the findings in simple ways. My work involves statistical method development and analysis in areas of personalized medicine from big data in genetics, ie cater medicines based on patients’ genetics. Currently principal scientist & leading a group of data analysts in a joint project between Stanford, UCSF and Kaiser Permanente analyzing terabytes of genetic data of cancer patients.

Senior Data Scientist @ User engagement From September 2014 to Present (1 year 4 months) San Francisco Bay AreaResearch Scientist, Statistics and Statistical Genetics @ Natural language processing and Machine learning, Big data genetics @ Stanford. 4 method development papers and 10 applied papers, with 3 first authors and 3 published in Nature publishing group. From June 2010 to September 2014 (4 years 4 months) Data scientist (Consultant) @ User’s Engagement prediction : Model fit to predict the teacher's engagement score based on teacher's activities in Edmodo.com. First statistician in the team, responsible for statistical analysis of big data and visualization using R. From June 2013 to September 2013 (4 months) Statistician @ From November 2008 to June 2010 (1 year 8 months) Postdoctoral fellow, Statistics @ From June 2007 to October 2008 (1 year 5 months) Postdoctoral Fellow, Statistics @ From June 2007 to October 2008 (1 year 5 months) PhD @ From 2003 to 2007 (4 years)

Ph.D, Statistical Genetics @ University of Pittsburgh From 2003 to 2007 M. Stat, Statistics @ Indian Statistical Institute From 2001 to 2003 B. Sc, Statistics @ Presidency College From 1998 to 2001 Amrita Ray is skilled in: R, Statistical Modeling, Bioinformatics, Stata, Computational Biology, Machine Learning, Data Analysis, Data Mining, Population Genetics, Hypothesis Testing, Biostatistics, Genomics, Statistics, Big Data, Statistical Genetics, Bootstrap statistical techniques, NGS statistical methods, Python, Natural Language Processing