Professional Summary

●*+ years’ overall experience covering Software/Information Technology/Web Development and Data Science.

●7+ Years in Data Science.

●Expertise in Machine Learning, Deep Learning, Natural Language Processing, and Data Analytics Ml_Ops, Model Productionizing and Monitoring

●Projects involving Sentiment Analysis, Fraud Detection, Predictive Analytics, Artificial Intelligence

●8 years of Python development.

●Extensive work in Natural Language Processing and Predictive Analytics using Machine Learning Algorithms, Visualization Tools, and Web Deployment Technologies.

●Used Neural Networks, Trees, Clustering Algorithms, and Statistical Models to propel systems which perform Sentiment Analysis, Fraud Detection, Client Segmentation, Predictive Maintenance, Demand Forecasting.

●Business understanding, Data understanding, Data preparation, Modeling, Evaluation and Deployment.

●Experienced in practical application of data science to business problems to produce actionable results.

●Experience in Natural Language Processing (NLP), Machine Learning & Artificial Intelligence.

●Experience with AWS, Kubernetes, and Azure cloud computing.

●Spark (especially AWS EMR), Kibana, Node.js, Tableau.

●Able to incorporate visual analytics dashboards.

●Experience with a variety of NLP methods for information extraction, topic modeling, parsing, and relationship extraction.

●Knowledge on Apache Spark and developing data processing and analysis algorithms using Python

●Programming strength in Python, C#, C++, Java, SQL, R, Matlab, Mathematica, JavaScript

●Use of libraries and frameworks in Machine Learning such as NumPy, SciPy, Pandas, Theano, Caffe, Sci-Kit Learn, Matplotlib, Seaborn, TensorFlow, Keras, PyTorch, NLTK, Gensim, Urllib, Beautiful Soup

●Ability with algorithms, data query and process automation.

●Evaluation of datasets and complex data modelling.

Technical and Project Team Collaboration Skills

LEADERSHIP - Push project goals, determine business use cases, and mentor/lead teams

QUALITY - Continuous improvement in project processes, workflows, automation and ongoing learning and achievement CLOUD Analytics in cloud-based platforms (AWS, MS Azure, Google Cloud)

CLOUD - Analytics in cloud-based platforms (AWS, MS Azure, Google Cloud)

ANALYTICS - Data Analysis, Data Mining, Data Visualization, Statistical Analysis, Multivariate Analysis, Stochastic Optimization, Linear Regression, ANOVA, Hypothesis Testing, Forecasting, ARIMA, Sentiment Analysis, Predictive Analysis, Pattern Recognition, Classification, Behavioral Modeling

PROGRAMMING LANGUAGES - Python, R, SQL, Java, MATLAB, Mathematica, C#, C++, JavaScript, PHP

LIBRARIES - NumPy, SciPy, Pandas, Theano, Caffe, SciKit-learn, Matplotlib, Seaborn, Plotly, TensorFlow, Keras, NLTK, PyTorch, Gensim, Urllib, BeautifulSoup4, PySpark, PyMySQL, SQAlchemy, MongoDB, SQLite3, Flask, Deeplearning4j, EJML, DPLYR, GGPLOT2, Reshape2, TIDYR, PURRR, READR, Apache, Spark, MapReduce, WPF, Entity Framework Core, Node.js

DEVELOPMENT - Git, GitHub, GitLab, Bitbucket, SVN, Mercurial, Trello, PyCharm, IntelliJ, Visual Studio, Sublime, JIRA, TFS, Linux, Unix

DATA EXTRATION AND MANIPULATION - Hadoop HDFS, Hortonworks Hadoop, MapReduce, Cloudera Hadoop, Cloudera Impala, Google Cloud Platform, MS Azure Cloud, SQL, NoSQL, Data Warehouse, Data Lake, SWL, HiveQL, AWS (RedShift, Kinesis, EMR, EC2, Lambda)

NATURAL LANGUAGE PROCESSING - Document Tokenization, Token Embedding, Word Models, Word2Vec, FastText, Bag Of Words, TF/IDF, Bert, Elmo, LDA.

MACHINE LEARNING - Supervised Machine Learning Algorithms (Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Random Forests, Naïve Bayes Classifiers, K Nearest Neighbors), Unsupervised Machine Learning Algorithms (K Means Clustering, Gaussian Mixtures, Hidden Markov Models, Auto Encoders), Imbalanced Learning (SMOTE, AdaSyn, NearMiss), Deep Learning Artificial Neural Networks, Machine Perception

APPLICATIONS - Machine Language Comprehension, Sentiment Analysis, Predictive Maintenance, Demand Forecasting, Fraud Detection, Client Segmentation, Marketing Analysis

Professional Work Experience

January 2022 – Current

Location: Piscataway, NJ (Remote)

Position: Data Scientist

Company: Verizon

Experience Summary:

Verizon is a leading provider of technology, communications, information, and entertainment products. I worked with a Business Improvement/Technology team mandated to improve NLP models used to read IT emails to automate actions in accordance with the appropriate categorization. I was the only Data Scientist (DS) on my team and the DS that developed the use case I was meant to improve had been gone for over a month. I performed due-diligence background knowledge acquisition to position myself to properly assess and understand the model system under scrutiny. I quickly transitioned as point-of-contact for product owners and just as quickly expanded my knowledge regarding the various moving parts of the project use case. I then applied myself in an environment in which multiple key stakeholders were either leaving the company or going on extended leaves. My work involved engaging with multiple team members operating from various time zones spanning from PST to GMT.

●Juggled workload for 2 use cases simultaneously. Each case presented unique business logistics and challenges.

●Read, studied, and understood Data Scientist code to formulate a plan to improve production model performance.

●Illustrated and articulated points of interest and pain points to product owners to reinforce faith in the project.

●Directed meetings for both Scrum and product owners.

●Collaborated with my team on use case efforts with respect to 4 different time zones.

●Created Excel files to showcase the origin of features being actively used in the use case for presentation to product owners.

●Met and consulted with product owners to better define success and to establish an appropriate metric to assess performance to replace the previous one.

●Pointed out inconsistencies and potential pain points in the production roll out.

●Modified scripts to allow for deeper model hypertuning.

●Greatly improved preprocessing.

●Created a system to efficiently utilize 4 different model versions.

●Created turnover folders, files, and documentation to allow for continuation on my set solution itinerary.

●Used acquired business knowledge to perform quality assurance on multiple model systems.

●Assessed and labeled over 1000 observations worth of data for cross-validation and QA purposes.

●Developed scripts to calculate metrics.

●Used metrics, tables, and business knowledge to articulate a story and create an itinerary of model improvement for product owners

●Improved model performance by 65%.

●Applied Ktrain for NLP model training.

●Hands on with Pandas open-source Python library.

●Used Python/R to manipulate, analyze and visualize large data sets.

●Utilized Numpy Python Library tool for performing mathematical and logical operations on arrays.

●Hands-on with TensorFlow and Keras deep learning tools.

●Worked with Seaborn visualization/reporting tool.

June 2019 – January 2022

Location: San Francisco CA

Position: Senior ML-Ops Engineer

Company: Levi Strauss & Co.

Experience Summary:

iSmile Technologies is a global technology services company that helps businesses compete by adopting disruptive technologies such as advanced analytics, Artificial Intelligence, big data, cloud, databases, DevOps, and infrastructure management to advance innovation and increase agility. As lead AI/ML-Ops engineer, I designed and implemented an efficiency manufacturing solution using AWS Batch and Docker Containers for a computer vision model prepared to find the cracks, anomalies, and other blockage part in the sewers, so that it can be rectified at priority basis for the proper functioning of sewage flow. The models were built using Sagemaker and Scheduled using Adobe Airflow.

●Built a personalized in-session product recommendation engine.

●Wrote scripts in Python that automated text summarization and clustering.

●Next-Best offer prediction.

●Designing Microassortments for Next-Gen stores.

●Anomaly detection and Root Cause Analysis.

●Prepared data for collaboration with machine learning models.

●Unified consumer profile with probabilistic record linkage.

●Visual search for similar and complementary products.

●Architected, built, maintained, and improved new and existing suite of algorithms and their underlying systems.

●Analyzed large data sets apply machine learning techniques and develop predictive models, statistical models and developing and enhancing statistical models by leveraging best-in-class modeling techniques

●Implemented end-to-end solutions for batch and real-time algorithms along with requisite tooling around monitoring, logging, automated testing, performance testing and A/B testing.

●Worked closely with data scientists and analysts to create and deploy new product features on the ecommerce website, in-store portals, and the Levi’s mobile app.

●Established scalable, efficient, automated processes for data analyses, model development, validation, and implementation.

●Implemented deployment solutions using TensorFlow, Keras, Docker, and Elastic Kubernetes Service.

●Implemented Model Drift Monitoring and Retraining Strategies.

●AWS, GCP, Azure.

●SageMaker Model transformation into dedicated preprocessing, inference, and model validation scripting.

●ECR, EMR, Azure Kubernetes Service Experience.

March 2017 – June 2019

Location: Atlanta, GA

Company: Bank of America

Position: Data Scientist

Experience Summary:

At Bank of America, I worked as a Natural Language Processing expert and model architect where I built, trained, and tested multiple Natural Language Processing models which classified user descriptions and wrote SQL code based on user questions. The goal of the project was to centralize and search for Splunk dashboards within the Bank of America network and to create an A.I. assistant to automate the coding process to extract information from these dashboards.

●Used Python, and SQL to collect, explore, analyze the structured/unstructured data.

●Used Python, NLTK, TensorFlow to tokenize and pad comments/tweets and vectorize.

●Vectorized the documents using Bag of Words, TF-IDF, Word2Vec, GloVe to test the performance it had on each model.

●Created and trained an Artificial Neural Network with TensorFlow on the tokenized documents/articles/SQL/user inputs.

●Performed Nearest Entity Recognition (NER) by utilizing ANNs, RNNs, LSTMs, and Transformers.

●Involved in model deployment using Flask with a REST API deployed on internal Bank of America systems.

●Wrote extensive SQL queries to extract data from the MySQL database hosted on Bank of America internal servers.

●Built a deep learning model for text classification and analysis.

●Performed classification on text data using NLP fundamental concepts including tokenization, stemming, lemmatization, and padding.

●Performed EDA using Pandas library in Python to inspect and clean the data.

●Visualized the data using matplotlib and seaborn.

●Explored using word embedding techniques such as Word2Vec, GloVe, and Bert.

●Built an ETL pipeline that could read data from multiple macros, processed it using self-made preprocessing functions, and stored the processed data on a separate internal server.

●Used Datamodelr R package to document relational data.

●Automated ETL tasks and scheduling using self-build data pull-request functions.

November 2014 – March 2017

Location: Atlanta, GA

Company: Interactive Intelligence Company

Position: ML Scientist

Experience Summary:

Interactive Analytics is a software development firm. Established the NLP (Natural Language Processing) Lab at II to research ways to better interact with users in written form. Used TF-IDF and NLTK techniques to classify, segment and analyze sentiment of textual data. Implemented various text analysis projects and built-in house NIP libraries in Python to clean, preprocess and tokenize incoming textual data.

●Implemented application of various machine learning algorithms and statistical modeling like Decision Trees, Text Analytics, Sentiment Analysis, Naive Bayes, Logistic Regression and Linear Regression using Python.

●and determined performance.

●Interrogate analytical results to resolve algorithmic success, robustness, and validity.

●Use of a variety of NLP methods for information extraction, topic modeling, parsing, and relationship extraction.

●Developing, deploying, and maintaining production NLP models with scalability in mind.

●Implemented Agile Methodology for building an internal application.

●Use of knowledge databases and language ontologies.

●Wrote a Flask app to call CoreNLP for parts-of-speech and named entity recognition on natural English queries.

●Optimized SQL queries to improve performance of data collection.

●develop an estimate of uncertainties for the semantic predictions made by deep convolutional model.

●Derived high quality information, significant patterns from textual data source. Used Document Term Frequency and TF-IDF (Term Frequency- Inverse Document Frequency) algorithm to find information for topic modelling.

●Analyzed large data sets, applied machine learning techniques, and developed predictive models, statistical models and developed and enhanced statistical models by leveraging best-in-class modeling techniques.

●Design, develop and produce reports that connect quantitative data to insights that drive and change business.

●Implemented both Elmo and BERT embeddings to correctly encode text.

May 2013 – November 2013

Location: Milford, NJ

Company: Warren Heating & Cooling

Position: Software IT/Web Development

Warren Heating & Cooling is a HVAC service provider.

I worked on web site upgrades:

●Modified the web site’s PHP framework.

●Wrote new functions in Java and modified existing functions in Java.

●Modified multiple scripts written in JavaScript.

●Developed Web API functionality for data validation and back-end database communication using ASP.NET, C#, and SQL Server to support the development of front-end interfaces.

●Assembled unit tests for a variety of Web API scenarios using Visual Studio’s testing components.

Education

BS in Mathematics, Rowan University Galsboro

Contact this candidate