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Data Scientist Resume Example

Warren Taylor


A highly competent data scientist with five years of experience developing a wide range of innovative applications like Credit Card Fraud Detection, Stock Sentiment Analysis Model, and Customer support system. Ability to use (data) statistics and machine learning for finding complex data patterns that drive meaningful impact on the business. I am looking for the opportunity to build a challenging career and apply my skills in an innovative and simplified process. I enjoy working in a team and communicating data-driven results.


Data Scientist
Info-Tech Solutions New York
To predict house prices using time series analysis and neural networks
Identify factors that predict which employees will have the best performance and which will
benefit from a change in their job position using machine learning.
Designed the information architecture and model of an organization’s assets
Coordinated with the stakeholders on the project progress.
Involved in the continuous enhancements and finding the best solution.

Data Scientist Consultant
JP Morgan Asset Management Florida
Predicting the Stock price using sentiment analysis model.
Finding the stock market daily patterns and creating a meaningful solution to the business.
Identifying the Multi bagger stocks using machine learning and AI


  1. Python & R
  2. NLTK & OpenCV
  3. SQL & SAS
  5. PowerBI & Spotfire


Master of Computer Application
Cornell University Ithaca, New York

Bachelor of Computer Science
California Institute of Technology


Stock Price Prediction
JP Morgan Asset Management Florida
Use the Sentiment Analysis Algorithms to
understand the stock sentiments.
Project: Twitter Sentiment Analysis
Tools: NLTK, Python
Algorithms: Sentiment Analysis

Stock Recommender Systems
JP Morgan Asset Management Florida
Recommender systems have become the most
a popular feature of the stock market. We have
developed an artificial intelligent stock recommender
tool using the Deep Neural Networks
and classification algorithms
Tools: Python, sklearn
Algorithms: Deep Neural Networks, classification algorithms

Customer Support System
Data Scientist
Customer support has become a challenging job for
every business. We build a “customer support system” to address and support the needs of the customers.
Tools: OpenCV, Python
Algorithms: Convolution Neural Network and other facial detection algorithms


  • English
  • Spanish
  • Catalan
  • French

Personal Skills

  1. Communication
  2. Motivation to learn
  3. Result-oriented
  4. Analytical mind
  5. Enthusiasm & optimism
  6. Critical Thinking
  7. Presentation Skills
  8. Personal Skills


OpenCV, Python 20190110
AI Solution Tech Florida

Matplotlib & GCP 20190917
Cloud Solutions New York

#1 Data Scientist – Research Assistant

  • Developed and lead Arduino projects based on C++.
  • Lectured on C to approximately 200 students.
  • Assisted faculty with in-depth data research in both lab and office environments.
  • Used Excel to enter data into the project database and provided updates on a weekly basis.
  • Validated incoming data to check information accuracy and integrity while independently locating and correcting concerns.

#2 Data Scientist – Data Analyst

  • Analyzed different aspects in Blockchain to incorporate parking availability for the citizens of Detroit.
  • Planned and conceived computer systems using information engineering, data modeling, and structured analysis.
  • Expedited industry-wide recognition, company awareness, and valuable insights for Detroit Used programming capabilities in PHP, SQL and JavaScript and other libraries as needed.
  • Researched new technologies, software packages, and hardware products for use in website projects.
  • Applied knowledge of data modeling and statistical analysis to note trends and draw conclusions.

#3 Data Scientist – Research Fellow

Development of a tool to support decision making about the feasibility of breakwaters.
This work goes through 3 phases:

  • Phase 1: Acquisition and treatment of meteo-oceanographic data. The development of some routines with Python allowed us to speed up much of the mathematical treatment involved.
  • Phase 2: Use of a numerical model of sea waves. I created some algorithms that allow procedure automation based on data creation and management, using Python libraries such as Pandas, Numpy, Scipy, and Matplotlib.
  • Phase 3: Neuronal network application.

#4 Data Scientist – Responsibilities

  • Providing data analytical solutions to various problems along with business-driven data insights.
  • Contributing to designs and launches, innovative and complex analytic models, utilizing a blend of contemporary & traditional data mining techniques, which are applied to both structured & unstructured datasets.
  • Managing automation projects for the smooth functioning of various processes and portfolios
  • Participating in technology planning & direction, strategy development, leadership and implementation, business and operational transformation execution, business solution delivery, and business development.

#5 Data Scientist – Data Analyst

  • To predict high priority tickets using the Random Forest classifier.
  • Used predictive analytics such as machine learning and data mining techniques to Forecast the incident volume in different fields, quarterly and annually.
  • Coordinated with the clients to know about the business problem.
  • Involved in the continuous enhancements and finding the best solution.

#6 Data Scientist – Data Analyst

  • Performed Data cleaning, transformation, validation with the purpose of understanding or making conclusions from the data for decision-making purposes.
  • Implemented descriptive statistics and visualization techniques to check the data normality.
  • Selecting features, building and optimizing classifiers using machine learning techniques
  • Worked on the Account receivable data to predict potential Defaulters for the payment and tested the data with different algorithms (SVM, GBM, Random forest) and implemented with Random forest.
  • Worked on Services data and analyzed the data between orders inflow with Inventory and created a visualization.
  • Improved Model performance with the help of parameters tuning and customizing the tuning parameters.

#7 Data Scientist – Responsibilities

  • Organized large datasets to obtain actionable insights including finding innovative ways to combine fields
    of data and ensuring high-quality data management techniques.
  • Performed Data cleaning, transformation, validation with the purpose of understanding or making
    conclusions from the data for decision-making purposes.
  • Performed model building and data processing using Random Forest, logistic algorithm over a Policy
    issuance cycle time predictions. Used an ETL process to clean the data and feed it into ML.
  • For the travel claim request by using the Python and machine learning techniques like Clustering, Logistic identified the Key entity like flight number, seat number, travel date and time, Business class information and share to the corresponding team.
  • Worked on the classification use case and tested the model with Logistic, Random forest, XGB algorithm and deployed with the Random forest model based on confusion matrix validation. Calculated accuracy, precision, recall and F score and identified the best model

#8 Data Scientist – Data Analyst

  • Completed the requirements in data model tool using GEHC BI COE standards
  • Created and Maintaining System Design Specifications (SDS), DDL and indexing scripts.
  • Developed data architecture, data modeling by using the Erwin tool, and ETL mapping solutions and data warehouse consistency Assessed current technical architecture and estimated cost for technical components.
  • Designed the Entity model, CDS model, and logic in Greenplum.
  • Designed Summary, snapshot design based on requirements and responsible for query tuning and

#9 Data Scientist – Audit Analyst

  • Evaluate reinsurance risk valued at $2.1 trillion for systemic errors due to poor data quality, flawed business logic, and contract compliance.
  • Query large data sets in an Oracle environment using complex SQL programming and perform complex analysis in Excel
  • Frequent client contact and communication to work through difficult conversations regarding payment discrepancies.

#10 Data Scientist – Data Analyst II

  • Outbreak modeling for C-suite:
  • Using flu seasons proxies, the present potential financial impact from various infection scenarios on membership
  • Opioid Use Disorder predictive model:
  • Trained Xgboost model on 2.5M members’ claims history and
    demographic data to risk score for opioid use disorder.
  • Create KPI analyses for web portal and call center utilization:
  • Project expected decrease in calls due to web portal enhancements
    and subsequent headcount reductions via the Erlang model.
  • Support the Microstrategy clinical KPI dashboard:
  • Calculate performance goals using standard deviations as the goalposts.
  • Collaborate with clinical partners for new metrics and ad hoc deep dives

#11 Data Scientist – Data Analyst

  • Project in Python: to predict house prices using time series analysis and neural networks
  • Project in R: to identify factors that predict which employees will have the best performance and which will benefit from a change in their job position using machine learning (XGBoost)
  • Project in R: Predicting mortality of small cell lung cancer patients and identification of important prognostic

#12 Data Scientist

  • To analyze the health of underserved communities through data monitoring and the creation of visual dashboards.
  • Designs, prepares, tests, and debugs data frames using Pandas.
  • Develops, validates and executes algorithms and predictive models to understand the health of a population.
  • Identify factors that predict the overall health of individuals and their community.
  • Designed monthly visual data dashboard that simplified the trends and patterns within the community for the organization’s assets.
  • Coordinated with the stakeholders on project progress and presented results to community health workers and the
  • Involved in the continuous enhancements and finding the best solution to the system.
  • Created a data monitoring and manual for the organization to document the method of data monitoring.

#13 Data Scientist – Technical skills

  • Algorithms: Linear & Logistic Regression, Decision Tree, Random Forest, KNN, GBM, SVM, Naive Bayes,
    Clustering, Forecasting algorithms like ARIMA, Hybrid, HoltWinter, Stlm.
  • Languages: R-Programming, Python
  • Database: MS-SQL, MongoDB
  • Frameworks: Parallel Processing, 5C Model Framework
  • Domain: IOT, DTH, Election, FMCG, Retail

#14 Data Scientist – Core Competitions

  • Statistical Analysis
  • Data Modelling
  • Machine Learning
  • Data Analytics
  • Project Execution
  • Project Architect
  • Client Reporting