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Data Scientist

Location:
New York, NY
Posted:
August 16, 2022

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Resume:

PROFESSIONAL SUMMARY

•Professional in the field of Data Science with 8+ years of experience in statistical analysis, data analytics, data modeling, and creation of custom algorithms and overall 14+ experience in Information Technology.

•Application to the disciplines of machine learning and neural networks using a variety of systems and methods in training algorithms with different could platforms.

•Hands-on with NLP and artificial intelligence and computer vision technologies.

•Contrive scale analytics solutions to Big Data with Hadoop, Spark/PySpark, and other Big Data tools.

•Use statistical packages in Python, R together with SQL to build complex statistical models for predictive analysis, principal component analysis, and performing cluster analysis.

•Design, develop, and implement informative visualizations using Tableau software, publishing, and presenting dashboards, storyline on web and desktop platforms.

•Use 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.

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

•Apply business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

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

•Hands on experience in implementing linear discriminant analysis (LDA), linear and logistic regression models, Naïve Bayes, support vector machine classifiers, K nearest neighbors, Random Forests, Decision Trees, and neural networks while applying know how of Principle Component Analysis to strengthen Recommender Systems.

•Experienced with machine learning algorithm such as logistic regression, random forest, XGboost, KNN, SVM, neural network, linear regression, lasso regression and k-means.

•Skilled in statistical programming languages like R and Python, including Big Data technologies like Spark, Hadoop, HIVE, HDFS, and MapReduce.

•Experienced in Spark, Spark SQL, and PySpark visualization tools like Tableau and Ggplot2.

•Skilled in using Dplyr, Pandas, Numpy, Matplotlib, Seaborn, and Pandas in R and Python for performing Exploratory data analysis.

•Experience with Data Analytics, Data Reporting, Ad-hoc Reporting, Graphs, Scales, PivotTables and OLAP reporting with great spreadsheet skills in Microsoft Excel and Tableau.

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

TECHNICAL SKILLS

Analysis Methods: Bayesian Analysis, Inference, Models, Regression Analysis, Linear models, Multivariate analysis, Segmentation, Clustering, Sentiment Analysis, Predictive Analytics, Decision Analytics, Big data and Queries Interpretation, Advanced Data Modeling, Forecasting, Predictive, Statistical, Sentiment, Exploratory, Stochastic, Sampling methods, Forecasting, Design and Analysis of Experiments, Factorial Design and Response Surface Methodologies, Association Analysis

Analysis Techniques: Classification and Regression Trees (CART), Support Vector Machine, Random Forest, Gradient Boosting Machine (GBM), TensorFlow, PCA, RNN, Regression, Naïve Bayes

Deep Learning: Neural Networks, TensorFlow, Keras, Machine perception, Data Mining, Machine Learning algorithms,

Analytic Development: Python, Spark, R, SQL, R, MATLAB, Command Line, Markdown, SAS, SPSS, C/C++

Python Packages: Numpy, pandas, scikit-learn, TensorFlow, SciPy, Matplotlib, Seaborn, Bokeh, Numba

IDE: Jupyter, Spyder, PyCharm, RStudio, Visual Studio, Code Blocks

Version Control: GitHub, Bitbucket, Git, SVN

Machine Learning: Natural Language Processing & Understanding, Machine Intelligence, Machine Learning algorithms

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

Data Modeling: Bayesian Analysis, Statistical Inference, Predictive Modeling, Stochastic Modeling, Linear Modeling, Behavioral Modeling, Probabilistic Modeling, time-series analysis

Applied Data Science: Natural Language Processing, Machine Learning, Internet of Things (IoT) analytics, Social Analytics, Predictive Maintenance

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

Artificial Intelligence: text understanding, classification, pattern recognition, recommendation systems, targeting systems, ranking systems.

EXPERIENCE SUMMARY

Senior AI Engineer Mphasis, New York, NY 07/2020 - Present

Mphasis Limited is an Indian multinational information technology services and consulting company. Product teams often struggle to balance between release frequency and defect leakage. The trade-off is difficult to manage for teams involved in testing of products with many possible configurations, products with third-party dependency, products that need specific equipment and lab set-up, and hardware products. While almost every organization wants to achieve 100% test automation, not all product teams within the enterprise can achieve that due to the inherent nature of the products. To improve the quality of the testing and optimize beyond test automation, it is useful to use historical data to bring in the power of Artificial Intelligence and Machine Learning (AI/ML). As a Senior AI Engineer in the Mphasis Cognitive Quality Engineering Group, I led a team of Data Scientists, Machine Learning Engineers, and Data Engineers to design, build. and deploy AI automation solutions specific for manufacturing problems.

•Resolved issues with Jupyter notebook and Python scripts.

•Programmed logic and added appropriate Python libraries to develop models for Natural Language Processing and Machine Learning in the enterprise customer support space.

•Provided data required for analysis and performed quick data sanity checks on the raw data.

•Handled customer service by responding to the tickets, classifying ticket issues, and engaging in dialogue with Data Scientists.

•Built, tested, and deployed ML applications.

•Created and managed ML infrastructure in the Dev and UAT regions

•Managed data pipelines to prep data in Dev region.

•Provided support to Data Scientists to run experiments and improve models and qualify models.

•Applied strong problem solving and object-oriented design to data structures, algorithms, and other computer science fundamentals.

•Programmed in Python, Java, and C++.

•Applied machine learning and statistics methods such as classification, regression, recommendation, clustering, boosting, graphical models, etc.

Senior AI Engineer Datahinge, La Jolla, CA 05/2017 – 07/2020

Datahinge is an Artificial Intelligence start up. We envisioned and built a Golf AI Platform designed to provide complete analysis for top players. As Senior AI Engineer, I held a leadership role in the design implementation and testing of a broad series of tools, including computer vision-based golf swing analysis using Convolutional Neural Networks and Open CV. I led a team of Data Scientists and Machine Learning Engineers and served as principal liaison between the AI and the Dev-Ops and App development team.

•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.

•Implemented Machine Learning- based Computer Vision algorithms using Tensorflow and Keras.

•Deployed finished model on edge devices using Tensorflow-Lite.

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

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

•Performed In-Force management, including survival analysis, churn/retention analysis, and 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.

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

Data Scientist Santander Bank, Miami, FL 05/2014 – 01/2017

Served as senior level member of a team of Data Scientists responsible for the development, maintenance, and continued improvement of algorithms for the detection of fraud / financial crime. The team was also responsible for data monetization, which entailed documenting and cleaning data sources and performing analyses to determine which data sources should be kept internal and which could be monetized or sold. Once it was determined that a data set could be sold, the team would then derive data products based on particular use cases and perform market analyses to determine a market value for said data products.

•Utilized multiple machine learning approaches within Python, including time-series analysis, anomaly detection, and imbalanced binary classification.

•Performed cross-validation in Python on various approaches to determine best methodologies for accurately detecting fraud.

•Used Python for further validation to fine tune hyper parameters of models and to perform final model selection.

•Worked continuously with management, and other stakeholders, to ensure data monetization. initiatives were in line with overall company goals.

•Performed unit tests within project code repositories.

•Validated models continuously.

•Constantly and continuously worked to engineer and test new features from new data sources to improve signal quality for fraud and anomaly detection algorithms.

•Used Python and TensorFlow to build proofs of concept for new models, new features for existing models, and new data products, and then to productionize and deploy these as finished models, features, or products.

Expediting Manager PENN FLORIDA City, Delray Beach, 06/2012 – 05/2014

Represented Penn Florida at Mizner Golf and Country Club, providing catering and event-related needs. Increased efficiency, decreased ticket times, and awarded Employee of the Quarter.

WebMaster Citrus County Chronicle City, Crystal River Florida, 02/2008 – 05/2011

Built and maintained web presence for the newspaper. Wrote HTML and CSS code to display and update articles and historical data.

EDUCATION

Bachelors, Mathematics and Computer Programming

Saint Leo University

Associates in Physics

Florida Atlantic University



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