Lead Data Scientist Resume San Francisco, CA - Hire IT People

SUMMARY:

A Senior Data Scientist credited with leveraging expertise in mathematics and applied statistics with programming skills to create predictive models and tools that increase business opportunities and profitability.
Passionate about data and driven to uncover new insights in large sets of structured and unstructured data paired with a willingness to explore new territories to solve complex business problems.

TECHNICAL SKILLS:

Tools: SAS, SQL (Netezza, SQL Server, Teradata), KNIME, Matlab, Python, C/C++, Java, Perl, Linux, R

Modeling concepts: Machine Learning, Time series analysis, Clustering, Generalized Linear and Additive Models, Nonlinear Regression, Classification, Neural Networks, Decision trees, Text mining, OCR

Big Data Tools: Hadoop, MapReduce, HBase, Hive, Pig, Spark, Splunk

Special expertise: Marketing Mix Modeling and Optimization, AB Test Design and Evaluation, Customer Targeting and Segmentation, Insurance pricing, Fraud Detection

PROFESSIONAL EXPERIENCE:

Confidential, San Francisco, CA

Lead Data Scientist

Responsibilities:

Led analysis of advertising impact on customer behavior by creating marketing mix models using nonlinear regression and by designing and evaluating A/B experiments for various marketing activities; used models and tests to increase marketing effectiveness and overall profitability for national advertising campaigns (SAS)
Designed a data - warehouse framework to manage transaction-level ad exposure and web browsing behavior data (SQL Server)
Developed analytics reports based on those to deliver weekly updates about campaign results and created customized reports to support new business pitches (Netezza SQL)

Confidential, Berkeley, CA

Co-Founder & Analytics Lead

Responsibilities:

Created and implemented quantitative pair trading strategies using linear regression for a market neutral U.S. equities hedge fund
Developed research platform for model design and trading simulation, built trade execution engine for signal generation and order management (Matlab, Java, MySQL) using real-time and historical tick data

Confidential, Menlo Park, CA

Predictive Modeler/Manager

Responsibilities:

Developed predictive regression models for pricing and risk indication while leading a team of Predictive Modelers to produce accurate and highly effective solutions to achieve business goals (SAS, Emblem)
Built a logistic regression model to prioritize investigation of theft claims cut losses due to insurance fraud, consulted deployment team for nationwide implementation (SAS)
Built a rate indication model using time series analysis (SAS) to better anticipate future changes in loss ratios

Confidential, Denver, CO

Senior Predictive Modeler

Responsibilities:

Designed and created predictive regression models for P&C insurance clients for loss prevention, risk evaluation
Implemented key algorithms in proprietary SaaS modeling tool for company-wide use (Java)
Consulted clients in project management and business integration of predictive models with exceptional customer service and attention to detail

Confidential, San Francisco, CA

Co-Founder & Technology Lead

Responsibilities:

Managed as one of two founders all day-to-day activities of a small startup (IT, HR, accounting, web presence, Internet sales infrastructure)
Developed data warehouse for quantitative marketing of CPG across various media channels (SQL Server)
Performed ROI analysis for online and offline campaigns and improved customer targeting by creating more accurate customer profiles

Confidential, Houston, TX

Quantitative Analyst & Developer

Responsibilities:

Re-designed and expanded procedures for derivatives-based predictive modeling in an R&D company that designs, develops and applies advanced modeling technology for the financial markets operating a $100 million hedge fund. Held full responsibility for data ETL, cleaning, validation, and signal generation,
Created a suite of C++ classes for daily and intra-day equity and option data (incl. the calculation of the implied volatility and other key derivative statistics).
Fine-tuned existing basic strategies, enabled a more reliable and successful predictive signal used in trading, and improved the overall quality of option signals (Matlab).

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Lead Data Scientist Resume

San Francisco, CA

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