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Data Science

Location:
Chicago, IL
Posted:
April 27, 2021

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Professional Profile

Technical Skills Profile

Analytic Development: Python, Javascript, Matlab, SAS, Spark, SQL VBA, C++, C

Python Packages: Numpy, Pandas, scikit-learn, TensorFlow, SciPy, Matplotlib, Seaborn, Numba, SpaCy, NLTK, LightGBM, XGBOOST, CatBoost, Dask, Gensim

IDE: Jupyter, Spyder, MatLab, Visual Studio

Version Control: GitHub, Git,

Machine Learning: Time Series Prediction, Natural Language Processing & Understanding, Machine Intelligence, Generalized Linear Models, Machine Learning algorithms

Data Query: Azure, Google Cloud, Amazon RedShift, Kinesis, EMR; RDBMS, Snowflake, SQL and data warehouse, data lake and various SQL and NoSQL databases.

Deep Learning: Machine perception, Machine Learning algorithms, Neural Networks, TensorFlow, Keras.

Artificial Intelligence: text understanding, NLP, Computer Vision, customer behavior predictive modeling, classification, pattern recognition, targeting systems, ranking systems.

Analysis Methods: Advanced Data Modeling, Time Series Analysis, Forecasting, Predictive, Statistical, Sentiment Analysis, Exploratory, Stochastic Calculus, Bayesian Analysis, Inference, Models, Regression Analysis, Linear models, Multivariate analysis, Sampling methods, Forecasting, Segmentation, Clustering, Predictive Analytics, Big Data and Queries Interpretation, Design and Analysis of Experiments, Association Analysis

Analysis Techniques: Classification and Regression Trees (CART), Support Vector Machine, Random Forest, Gradient Boosting Machine (GBM), TensorFlow, Principle Component Analysis, Recurrent Neural Networks, Regression, Naïve Bayes

Data Modeling: Bayesian Analysis, Statistical Inference, Predictive Modeling, Stochastic Modeling, Linear Modeling, Behavioral Modeling, Probabilistic Modeling, Time-Series Analysis, Survival Analysis

Applied Data Science: Natural Language Processing, Machine Learning, Social Analytics, Predictive modeling

Soft Skills: Excellent communication and presentation skills; ability to work well with stakeholders to discern needs accurately, leadership, mentoring, coaching

Professional Experience

Roza Contractors Chicago IL

Principal Data Scientist

October, 2019 - Present

At Roza Contractors purchase order processing was automated using Optical Character Recognition (OCR) on scanned documents. Prior to my work, they were creating handwritten purchase orders and filing them in a filing cabinet. This took additional time and manpower when a record needed to be referenced. This system was automated so that scanned documents could be submitted to an API and the results of the OCR were stored in a SQL database for easy referencing.

Lead the development team and implemented the complete solution.

Wrote the code in Python and SQL

Implemented various NLP techniques to identify text fragments.

Used Tensorflow and Keras to implement word embeddings.

Used Google Tesseract and AWS Text Extract.

Used NLP technics to sort and classify documents.

Used AWS Redshift Data Warehouse and Boto 3 to access AWS Resources from Python.

Work with product development team for optimal product design, pricing and marketing strategy.

Developing hazard models for credit loss projection by applying Cox Proportional hazard model and logistic probability model with customer cohorts, and explore a Gradient Boosted Machine and Deep Neural Network

Oversee/Manage daily asset liability modeling production on AWS with Oracle database and other data formats

ROUTE Chicago IL

Machine Learning Engineer

April 2017 – September 2019

Route is a SaaS product for commercial cleaning companies. To supplement their application, an Object Detection model was created that could identify the type and number of “fixtures” at a location based on a camera phone photograph. In the cleaning space a “fixture” is any item in a space that needs to be cleaned. These items could be chairs, couches, tables, computer systems, desks, etc.. The model was deployed on edge devices using Tensorflow-Lite.

Developed a custom dataset for fine-tuning a deep neural network.

Fine-tuned a variety of image models with object detection heads.

Used both Single Shot Detection (SSD) and You Only Look Once (YOLO) object detection models.

Deployed finished model on edge devices using Tensorflow-Lite.

Build various statistical models Statistical algorithms involving Time Series analysis, Survival Analysis, Multivariate Regression, Linear Regression, Logistic Regression and PCA in financial projection

Lead the development of the expected profit projection engine by applying machine learning with financial engineering, actuarial science.

Build various statistical models Statistical algorithms involving Time Series analysis, Survival Analysis, Multivariate Regression, Linear Regression, Logistic Regression and PCA in financial projection

Perform inforce management including survival analysis, churn/retention analysis, risk identification

Used pre-trained models to visualize the feature maps in the intermediate layers and performed transfer learning

Used pre-trained models (VGG16, ResNets, Inceptions, DenseNet, U-Net, etc.) for transfer learning on small datasets

Design and implement the enterprise Financial Value-at-Risk model

Lead various cross-department projects and worked closely with internal stakeholders such as business teams, product managers, engineering teams

Worked on customer segmentation using an unsupervised learning technique clustering.

Rozalado Services South Bend, IN

Lead Data Scientist March 2015 – April 2017

Rozalado Services is a commercial cleaning company that takes a data driven approach to commercial cleaning. During my time there, I developed an NLP model that would read incoming emails and determine if a call needed to be scheduled. If so, the script would send an automated reply with a calendar that contained the open call slots of the salesperson.

Performed fund analysis for Investment Management, improving methodologies for modeling and hedging fund driven exposure, providing monthly fund dashboard, quantifying the risk characteristics of funds

Lead the development of customer key risk indicator using natural language processing (NLP) technical and LSTM to process text records.

Implemented ML and NLP solutions in Python

Developed portfolio replicating process for asset-liability portfolio using plain vanilla instruments for Asset-Liability/Economical Capital management

Used NLP techniques such tokenization, stop words, normalization, regularization, bag-of-words, and tf-idf for classification of emails

Performed EDA and build statistical visualizations for language used in emails

Developed RESTful API to pull emails from and placed into model pipeline

Hollywood Video South Bend, IN

Lead Data Scientist October 2013 – January 2015

Hollywood Video pivoted from a video rental chain to a website that partnered with Amazon to provide movie reviews and purchase suggestions. At that time I was responsible for business analytics, using statistical techniques to categorize new movies using Logistic Regression, to provide business insights with Tableau, and to help on their ‘social scoring’ algorithm, an early recommender system.

Performed Collaborative Filtering for ‘social scoring’ algorithm to match users with movies they may like.

Performed Content Based Filtering to match users with movies through ‘social scoring’ algorithm.

Maintained a database of users preferences and movie reviews to match users with movies.

Performed fund analysis for Investment Management, improving methodologies for modeling and hedging fund driven exposure, providing monthly fund dashboard, quantifying the risk characteristics of funds

Lead the development of customer key risk indicator using natural language processing (NLP) technical and LSTM to process text records.

Developed portfolio replicating process for asset-liability portfolio using plain vanilla instruments for Asset-Liability/Economical Capital management

Jacobs-Cathey Co. Waco, TX

Staff Data Science Associate

March 2011 – August 2013

Jacobs-Cathey Company is a heating and air (HVAC) company. I was in charge of load calculation for their residential clients and during my time there I digitized their customer data and created an algorithm that forecasted summer sales based on this data. In TX, HVAC is a seasonal business where most of their customers' problems would happen in the summer when temperatures often rise above the mechanical limits of air conditioning units. Based on their last maintenance time, the maintenance problem code, and other factors the model predicted a general sales forecast. This forecast was used to buy copper in the winter when the prices were low that would last through the summer when copper prices would rise exponentially.

Developed custom regression model for prediction of heat load in residential systems

Developed time-series analysis to forecast summer sales.

Used time-series analysis to predict amount of copper piping needed for summer sales.

Worked with key mathematicians and statisticians to build financial models and perform statistical analysis.

Developed models and documented algorithms for production Scenario Generator

EDUCATION

Baylor University Waco, TX

B.A. in Philosophy, December 2008

Specialization: Computational Finance, Numerical Analysis and Mathematical Programming

Northwestern University Chicago, IL

M.A in Philosophy, December 2011

Specialization: Formal Syntantics, Cognitive Sx and Semantics, Cognitive Sntics, Cognitive Science, Mathematical Modeling

Northwestern University Chicago, IL

PhD in Mathematical Logic, June 2018

Specialization: Church’s Typed Lambda Calculus, Computational Linguistics, Semantics

CERTIFICATES

Cognitive Science, Northwestern University

Full Stack Engineering, Northwestern University

Python Programming for Data Science, Promotable at 1871

Data Analytics for Business, Promotable at 1871

312-***-****

adlzpj@r.postjobfree.com

Experience

10 years of experience

Data Science and Machine Learning Engineer

Competencies

Quantitative Modeling

Machine Learning

Predictive Modeling and Analytics

Market Risk Modeling

Natural Language Processing

Fraud Prevention

10 years of quantitative modeling, data science, Machine Learning and Python programmer

Extensive experience of cross-department project management

Experience in the application of Naïve Bayes, Regression Analysis, Neural Networks/Deep Neural Networks, Support Vector Machines (SVM), and Random Forest machine learning techniques.

Experience in machine learning models, statistical models on big data sets using cloud/cluster computing assets with AWS and Azure.

Extensive experience on time series analysis and survival analysis

Work with product development department for optimal product design, pricing and marketing strategy

Hands on experience in credit risk modeling from hazard models, severity models and exposure at risk models

Extensive quantitative Natural Language Processing (NLP)

Convolutional Neural Networks, Computer Vision

Customer behavior predictive modeling on lapse/churn, withdraw

Fraud detection and prevention with financial transactions

Extensive model validation experience in data science, and quantitative modeling

Excellent communication skills (verbal and written), able to communicate with clients/stakeholders and team members.



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