Data Scientist / Developer Resume

TECHNICAL SKILLS

Programming: SQL, Python, R, MatLab Knime, Power BI, Tableau

Mathematics: Advanced Probability & Statistics, Differential Equations, Computer Numerical Algorithms

Data Science: Classification, Random Forests, Data Mining, Linear Regression, NLTK, Decision Trees, RHadoop

Methodologies: Waterfall, Agile - Scrum

PROFESSIONAL EXPERIENCE

Confidential

Data Scientist / Developer

Responsibilities:

Perform in a team setting to redesign and redevelop our in-house Reinsurance web-application
Optimized & Rewrote SQL Stored Procedures and Queries in order to streamline application landing page-loading
Work closely with stakeholders, product owner, subject matter experts, team members and business side of the application being rewritten
Included understanding the actuarial science that went behind Reinsurance financial risk decision making
Leveraged analytical tools for data science and visualization such as PowerBI, Knime and Tableau
Coordinated & Participated in the company’s first Hack-a-thon
Created conversational AI that uses data science techniques to predict risk based on historical data
Participated in writing Python application that automated Broker Submission Intake Forms using Text Mining
Used NLTK, Scikit Learn, Pandas, and NumPy Packages

Confidential

Policy Management Data Analyst

Responsibilities:

Analyze policy information to extract necessary data to complete WINS program coding entry for approximately 500-1000 policies per month
Collaborate with Underwriting, Technology, and Finance departments to solve data errors and discrepancies related to policies to secure accurate revenue realization of $75 million annually
Maintain WINS Interfacing program by leveraging error recognition techniques using in-house software
Provide technical WINS coding assistance to team members during periods of overflow in various policy types

Confidential

Financial Data Analyst Intern

Responsibilities:

Designed the composition of variables within mathematical risk parity models to predict performance outcomes measured against the S&P 500
Utilized data acquisition and data scrubbing from various financial data bases, including Bloomberg and US Treasury, to accurately simulate possible financial investment opportunities
Constructed and executed over 150 iterations of the simulated risk parity fund in MatLab and Excel with coinciding graphics to clearly explain a net-positive 17.4% return against the S&P 500
Presented positive results in a 40-page cohesive proposal to the CEO of the hedge fund for investment consideration in excess of $100 million