Data Scientist

SUMMARY

Over ** years in consulting with hands-on and deep experience in statistical analysis and machine learning using industry-wide statistical tools

Manipulating and analyzing complex, high-volume, and high-dimensionality data from varying sources

Provide data-driven insights to clients for decision making purposes

Advise on new analytic technologies to help clients achieve business goals

Support business development team by identifying analytics service offerings and creating use cases to solve business issues for clients

Creative and tenacious problem solver involved in all stages of technology projects from analysis, planning and design through deployment, support, testing and documentation; strong ability for adapting quickly to technology changes, and successfully taking on new tasks and challenges

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

Tools used:

Statistical: Python (sklearn, NumPy, Pandas, SciPy, NLTK), 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, Education & Government Agencies

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

-Discover patterns and signals in data using data mining techniques such as patterns in transaction rejections at point-of-sale, consumer purchase patterns, etc.

-Develop large scale data analytic solutions in machine learning such as regressions, KNN, random forest, SVM, K-means, NN, 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 generated actionable information for decision making purposes

-Design and implemented 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

-Provide design input specifications, requirements, and guidance to software engineers for algorithm implementation for product development

-Coordinate tactical-related business requests for Data Analytics development enhancements

-Actively engage with the business partners to fully understand the business requirements and translate into appropriate models

-Provide guidance to data collection teams on test protocols including design of experiments, sample size, and statistical distributions

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

-Helped with data planning and identifies potential problems that future data users may encounter

-Explored, developed and modeled new data sources to enable self-service

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

-Implemented appropriate processes for defining data requirements with external and internal partners

-Coordinated internally & externally to define, document, and execute the setup, quality, cadence, and method of delivery of data files

-Troubleshoot and problem-solving technical/data-related issues identified through requirements gathering, testing, and/or user-reported escalations

-Supported post-production implementations, coordinated and implemented data fixes

-Ensured accuracy of data by developing methods to check for discrepancies

-Built and maintained data mapping and import and export rules of a wide range of source files

EDUCATION

M.Sc., Applied Economics and Statistics, University of California – Berkley 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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