SAM BELLOWS

DATA SCIENCE MACHINE LEARNING NLP EXPERT

CONTACT

adczcq@r.postjobfree.com

650-***-****

New York, NY

linkedin.com/in/SBellowsDataScience

github.com/sbellows1

PROFILE

Data Scientist who specializes in

building recommender systems and NLP

models.

EDUCATION

Expected December 2020

CUNY School of Professional Studies

Master of Data Science

2015

New York University

Bachelor of Fine Arts

TECHNICAL SKILLS

Python, R, SQL

Machine Learning

Natural Language Processing

Collaborative Filtering and Matrix

Factorization

Statistics and Probability

Data Visualization

Calculus and Linear Algebra

Data Cleaning and Wrangling

Git, Excel, Tableau

SOFT SKILLS

Communication

Collaboration

Critical Thinking

Problem-Solving

Attention to Detail

Team Player

EXPERIENCE

2019 - PRESENT

Independent Data Scientist

NLP Models

Developed a sentiment analysis classifier using Naïve Bayes to improve AUC by 40% over the baseline model. The model would allow companies to quickly find and address negative feedback to improve customer experience and customer retention.

Created a highly scalable RNN sentiment classifier using Keras that increased F1 score by 28% over the baseline model and significantly outperformed all traditional machine learning models. MovieLens Recommender System

Created a collaborative filtering recommendation engine using a FunkSVD algorithm that improved recommendation RMSE by 23.5%. This could potentially lead to large increases in customer satisfaction or an increase in sales depending on the use case.

Used statistics to create model basis and included hyperparameter optimization and regularization.

Salary Prediction Model

Created an end to end machine learning pipeline including an ETL pipeline, feature engineering, multiple machine learning models, and hyperparameter optimization that resulted in a 47.5% decrease in model error compared to the baseline model.

Employed Object Oriented Programming and other best coding practices for readable, reusable, and professional code.

Saved results to a SQL database for ease of access. 2015–Present

Classroom Instructor A-List Education

Designed and taught SAT classes by investing in each individual student and providing personalized feedback and goals.

Continuously improved my instruction and student results by incorporating both mentor and student feedback.

Achieved an average score increase of 120 points per student on the SAT, a 71% increase over the national average score increase.

Stayed organized and prioritized effectively, helping me teach 15-20 classes per year, or 300 – 400 students.

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