Data Scientist

SUMMARY

Over ** years in Data Science, statistical analysis and machine learning using industry-wide tools

Customer-facing consultant to Fortune-500-companies advising on machine learning methods

Provide data-driven insights to clients for decision making purposes

Advise on new analytic technologies to help clients achieve business goals

Ability to communicate complex quantitative analysis, analytic approaches and findings in a clear, precise, and actionable manner

Tools:

Statistical: Python (sklearn, NumPy, Pandas, SciPy, Seaborn), PySpark, R

Big Data: Hadoop, Spark in Databricks Platform

Visualization: Tableau, Matplotlib

Databases: SQL, NoSQL (MongoDB)

EXPERIENCE

Principal Data Scientist Jan’2012 to date

Accenture, San Jose, CA

Client Sectors: Manufacturing, Finance, Telecom, Technology, Government Agencies, etc.

Note: Almost all assignments were completed in Python

-Assume the principal data scientist role in our Business Analytics engagements

-Develop large scale data analytic solutions in machine learning such as regressions, KNN, random forest, SVM, K-means, etc. to solve classification and clustering problems

-Build natural language processing (NLP) and text analytic models such as document retrieval, topic models, sentiment analysis, etc.

-Draw conclusions from data and generate actionable information for decision making purposes

-Design and implement machine learning algorithms, probabilistic and statistical algorithms

-Work within a team of other data scientists to develop prototypes of the algorithm to validate assumptions and outcomes

Senior Data Analyst Jan’2000 to Dec’2011

Apple, Cupertino, CA

-Acted as a thought partner and domain expert on data and helped in driving decision making

-Identified opportunities to scale actionable learning and make strategic recommendations in a time-bound delivery focused team environment

-Partnered with other business data analysts and internal stakeholders to understand reporting requirements

-Partnered with data engineers to design supporting data models such that data integrity rules are established as per lines-of-business requirements

-Delivered dashboards using Apple’s internal data reporting platform

-Communicated complex data in a clear dashboard view to marketing, operations, product management, engineering, business, amongst other teams

-Built tools and dashboards that empowered stakeholders, enabling them to access data and draw insights in a self-serve manner

EDUCATION

M.Sc., Statistics & Applied Economics, University of California – 1999

CERTIFICATIONS

Apache Spark – UC Berkeley (in progress)

Machine Learning – University of Washington 2016

BI &SAS Analytics Software – UC Berkeley 2015

Hadoop & Big Data – IBM 2014

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