Data Scientist Resume AI - Hire IT People

TECHNICAL SKILLS:

Primary Languages: Python, SQL

Secondary Languages: R, Java, Tableau

Data Engineering: HDFS, Spark, Neo4J, MongoDB, JSON, API, Web Scrape

Python Packages: Sklearn, Scikit - Learn, Numpy, Pandas, Tensorflow, Matplotlib, Plotly, Seaborn

Machine Learning: K-Nearest Neighbors, Support Vector Machine, Principal Component Analysis, Density-Based Scan, Latent Dirichlet Allocation

WORK EXPERIENCE:

Confidential, AI

Data Scientist

Responsibilities:

Developed architecture for dynamic anomaly detection and clustering in large-scale, streaming graphs
Developed machine-learning conversational guide based on utility of conversation
Including features and models drawn from domain knowledge in psychotherapy, sociology, and neuropsychology
Processed lyrics and generated rhyming text seeds with Neo4J and Python
Developed Triphone2Vec rhyme and accent detection algorithm

Confidential

Data Science Interns

Responsibilities:

Identified new market coverage possibilities for venture lending and methods for machine learning to recommend likely deals of interest to SVB staff
Graph-based analysis of venture capital ‘social networks’
Developed recommender system capable of predicting early stage deals of interest to critical late stage investors
Performed random walk clustering of investor networks and validated the results via known networks
Trained ERGM (exponential random graphical model) as a means of investigating investor closeness
Utilized investor participation data and neural network to predict likelihood of negative venture debt performance as a classification problem
Utilized matrix decomposition for broad, sparse dataset
Training accuracies averaged 80%
Optimized sample portfolio performance via elimination of high-risk deals based on likelihood estimate and business decision-making utility
Test portfolio performance improved by over 10%
Developed query-ready graph prototype, including integrating internal and external data sources
Utilized Density Based-Scan to identify networks of high-value investors

Confidential

Senior Financial Data Analyst/Data Operations Specialist

Responsibilities:

Sole analyst and data ops specialist for Confidential -mortgage mortgage backed securities and applications. Responsibilities included:
Validating new cashflow models against origination documents. Importing new model and data into Structured Finance Workstation deal library and SQL database. Maintaining relational database.
Providing quality assurance for monthly releases of data from the point of trustee release of data through SQL transformation to deal library and final published approval for client use
Client-facing communication, including providing domain expertise and customer service support
Extensive data munging in SQL and Excel
Trained and audited new hires and outsourcing team
Developing QA procedures and documentation for monthly data
Developed, wrote, and implemented QA logic, successfully automating 70% of approvals from a previously manual process
Oversaw 100% growth in the size of the Confidential library of deals
Oversaw universal data backfill of Confidential historical data
Designed and implemented projects to supplement and improve upon trustee property level data. Included extension of property default logic and development of key fields such as net-operating income and loan-to-value ratios

Confidential

Research Assistant

Responsibilities:

Researched, compiled, formatted, and summarized data sets based on potential for use in economics textbook, selecting for a combination of student interest and relevance to econometric study

Confidential

Assurance and Advisory Intern

Responsibilities:

Audited Charles Schwab website transition, trading process, and authorization, including trading floor process walkthrough, for Financial Services Risk Management and Internal Audit team