Senior Data Scientist

Jimi Cha

Phone- 214-***-**** Email- ********@*****.***

Acknowledged as a high performing professional with well-honed skill sets in this field, I have rendered my best efforts to meet or exceed the expectations and demands of the organization.

DATA SCIENTIST/ MACHINE LEARNING ENGINEER / AI

Executive Snapshot

Overall IT experience of 9 years, years of expert involvement in Artificial Intelligence, Generative AI, Data Mining, Deep Learning, Predictive Analysis, and Machine Learning Engineering (ML Ops) with big datasets of structured and unstructured data; Proficient in managing entire data science project life cycle and actively involved in all the phases of the project life cycle.

Synopsis

Strong analytical skills with a background in NLP, Computer Vision, Generative AI, Statistical Machine Learning, Big Data, Cloud Computing, Predictive Analytics, Machine Learning deployment, and maintenance.

Sr. Data Scientist / AI with expertise in Data Extraction, Data Modeling, Data Wrangling, Statistical Modelling, Data Mining, Machine Learning Engineering, ML Ops, and Data Visualization

Excellent academic credentials; Currently pursuing certification in Academy Database Programming backed by Master's in computer science from University of Southern California, - Bachelor of Science in Computer Science, University of California, Irvine, CA

Demonstrated excellence in Natural Language Processing (NLP) methods, in particular BERT, ELMO, word2vec, sentiment analysis, Name Entity Recognition, and Topic Modelling Time Series Analysis with AR, MA, ARIMA, GARCH, SARIA, LSTM, RNN, Prophet, and ARCH Models.

Well-versed in using various packages in Python and R like Pandas, NumPy, SciPy, Matplotlib, Seaborn, TensorFlow, Scikit-Learn, and ggplot2.

Broad experience in 3rd-party cloud resources: AWS, Google Cloud, and Azure

Possess a deep understanding of applying Naïve Bayes, Regression, and Classification techniques as well as Neural Networks, Deep Neural Networks, Decision Trees, and Random Forests.

Experience in working with and querying large data sets from big data stores using Hadoop Data Lakes, Data Warehouse, Amazon AWS, Cassandra, Redshift, Aurora, and NoSQL

Competent in statistical analysis programming languages such as R and Python (including Big Data technologies such as Spark, Hadoop, Hive, HDFS, and MapReduce).

Proficient in applying statistical analysis and machine learning techniques to live data streams from big data sources using PySpark and batch processing techniques.

Successful in leading teams to productionize statistical or machine learning models and create APIs or data pipelines for the benefit of business leaders and product managers.

Good knowledge of creating visualizations, interactive dashboards, reports, and data stories using Tableau and Power BI.

Proficient in all Supervised Machine Learning techniques which include:

oNaïve Bayes, Classifiers, K-Nearest Neighbors, Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, Random Forests, Neural Network Architectures, XGBoost, Survival Modeling

oNumPy stack (NumPy, SciPy, Pandas, and matplotlib) and Sklearn.

oTensorFlow and PyTorch for building, validating, testing, and deploying reliable deep learning algorithms for specific business challenges

oStatistical models on large data sets using cloud computing services such as AWS, Azure, and GCP.

oEnsemble algorithm techniques, including Bagging, Boosting, and Stacking

oBroad experience in 3rd-party cloud resources: AWS, Google Cloud, and Azure

oSuccessful in leading teams to productionize statistical or machine learning models and create APIs or data pipelines for the benefit of business leaders and product managers.

Possess knowledge of Natural Language Processing (NLP) methods, in particular BERT, ELMO, word2vec, sentiment analysis, Named Entity Recognition and Topic Modeling; Time Series Analysis with ARIMA, SARIMA, LSTM, RNNs

Well-versed in building unsupervised approaches such as K-Means, Gaussian Mixture Models, and Auto-Encoders. Program in R, Python, SQL, Spark, Scala, and MATLAB.

Efficiently handled visualizations using R-Programming (dplyr, ggplot2, plotly), Python (matplotlib, seaborn, plotly and Dash), and Tableau for end-user ad-hoc reporting.

Successfully designed custom BI reporting dashboards using Shiny, Shiny dashboard, and Plotly for providing actionable insights and data-driven solutions.

Expertise in working with and querying large data sets from big data stores using Hadoop Data Lakes, Data Warehouse, Amazon AWS, Cassandra, Redshift, Aurora, and other NoSQL and SQL data stores

Proficient with the identification of patterns in data and using experimental and iterative approaches to validate findings.

Working alongside Data Engineers to build efficient ETL pipelines for algorithms and solutions. Possess advanced predictive modeling techniques to build, maintain, and improve real-time decision systems.

Experienced in employing Python, R, MATLAB, SAS, Tableau, and SQL for data cleaning, data visualization, risk analysis, and predictive analytics.

In-depth knowledge of statistical procedures that are applied in both Supervised and Unsupervised Machine Learning problems.

Developing different computer vision models for object classification and image recognition

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

Capability to quickly gain an understanding of niche subject matter domains, and design and implement effective novel solutions to be used by other subject matter experts.

Experience implementing industry-standard analytics within specific domains and applying data science techniques to expand these methods using Natural Language Processing, implementing clustering algorithms, and deriving insights.

Cloud Computing within Amazon Web Services (AWS), Microsoft Azure and Google Cloud Plattform (GCP) with Machine Learning, Generative AI, Big Data and Data Engineering services

Technical Skills

Deep Learning:

Recurrent Neural Networks, LSTM Networks, Artificial Neural Networks, Transfer Learning, Convolutional Neural Networks, Segmentation, Auto Encoding/Decoding

Programming Languages:

Python, R, MATLAB, Linux, Latex

Optimization Techniques:

Dynamic Programming, Convex Optimization, Non-Convex Optimization, Linear & Non-linear Programming, Monte Carlo Methods, Network Flows, Gradient Descent

Statistical Methods:

Bayesian Statistics, Hypothesis Testing, Factor Analysis, Stochastic Modeling, Factorial Design, ANOVA

Data Systems:

AWS (RDS, RedShift, Kinesis, EC2, EMR, S3), MS Azure, SQL, MySQL, NoSQL, Spark, Hive, Hadoop

IDEs:

Spyder, Jupyter, PyCharm, RStudio, Eclipse, Sublime, VSCode

Machine Learning Frameworks:

TensorFlow, PyTorch, Keras, Caffe, LangChain, SKlearn

Unsupervised Learning:

Mixture Models, Hidden Markov Models, K-means Clustering, Hierarchical Clustering, Centroid Clustering, Principal Component Analysis, Singular Value Decomposition

Supervised Learning:

Naive Bayes Classifiers, Linear Regression, Logistic Regression, ElasticNet Regression, Multivariate Regression, Support Vector Machines, K-Nearest Neighbours, Decision Trees, Random Forests, Natural Language Processing, Time Series Analysis (ARIMA, G-ARCH, Prophet, RNNs), Survival Analysis (Poisson Regression, Cox Proportional Hazards, HMMs)

Python Libraries:

TensorFlow, NumPy, Pandas, SciPy, matplotlib, scikit-learn, Keras, PyTorch, PyBrain, Caffe, NLTK, StatsModels, Seaborn, Selenium, Flask, marshmellow, requests, sqlalchemy, SQLite

Development Tools:

GitHub, Git, IPython notebook, Trello, Jira, SVN

Cloud computing

Amazon Web Services, Microsoft Azure, Google Cloud Plattform

Professional Experience

Since March 2022 with BitMart Exchange – New Jersey

As a Sr. Data Scientist / Generative AI Engineer

(BitMart is a company that delivers a global digital asset trading platform that provides

real-time trading services including Bitcoin (BTC), Ethereum (ETH) & Tether (USDT) trading)

This Investment Portfolio project centers on Natural Language Processing and Time-Series Analysis for Investment Portfolio Management. The project involves creating a data model to analyze news in predictive analytics relating to stock trending over 6-month periods. This analysis is used to re-balance stock portfolios.

Responsible for exploring various algorithmic trading theories and ideas.

Successfully applying data mining and optimization techniques in B2B and B2C industries and Machine Learning, Data/Text Mining, Statistical Analysis, and Predictive Modeling.

Utilized Amazon Web Services (AWS) and Microsoft Azure for deploying and managing machine learning models.

Implemented CI/CD pipelines for machine learning models using AWS SageMaker and Azure ML.

Deployed ML models as microservices using Docker and Kubernetes on AWS and Azure cloud platforms.

Managed model versioning and tracking using MLflow and Azure Machine Learning

Utilized transfer learning techniques to adapt pre-trained models to specific domain requirements

Generative AI and Large Language Models (LLMs)

Developed and fine-tuned large language models (LLMs) for text generation and sentiment analysis

Implemented transformer-based models (e.g., BERT, GPT-3) for various NLP tasks and Generative AI

Deployed LLMs on cloud platforms for scalable and efficient inference

Worked closely with stakeholders to ensure the ethical and responsible deployment of generative AI models, addressing concerns related to bias, fairness, and transparency.

Provided mentorship and guidance to junior data scientists and AI practitioners, fostering a culture of continuous learning and professional development

Aggregated and normalized data from various sources. Analyzed and visualized external market data and internal data.

Utilizing predictive modeling by making use of state-of-the-art methods.

Executing advanced machine learning algorithms utilizing Caffe, TensorFlow, Scala, Spark, MLLib, R, and other tools and languages needed.

Created an Object Character Recognition (OCR) process to automatize handwritten document scanning and analysis with different computer vision and Natural Language Processing techniques.

Produced operations, financial, and market product analytics. Interpreted data, and analyzed results.

Created reporting visualizations to automate weekly and monthly reports.

Developed queries in requirements management tools using statistical techniques to create ad hoc and recurring reports

Conducted the preliminary data exploration to ensure the modeling feasibility.

Involved in programming and scripting in R, Java, and Python.

Work closely with stakeholders to ensure the ethical and responsible deployment of generative AI models, addressing concerns related to bias, fairness, and transparency

Developing a data dictionary to create metadata reports for technical and business purposes.

Building reporting dashboard on the statistical models to identify and track key metrics and risk indicators.

Performing boosting method on predicted model to optimize the efficiency of the model.

Extracting source data from Amazon Redshift on the AWS Cloud platform.

Parsing and manipulating raw, complex data streams to prepare for loading into an analytical tool.

Exploring different regression and ensemble models in machine learning to perform forecasting.

Developing new financial models and forecasts.

Improving efficiency and accuracy by evaluating models in R.

Utilizing TensorFlow to design deep learning models.

Applied Machine Learning (ML), deep learning (DL), and natural language processing (NLP) in Artificial Intelligence (AI) applications.

Provide mentorship and guidance to junior data scientists and AI practitioners, fostering a culture of continuous learning and professional development

Worked well with stakeholders to accurately discern needs, advise on feasibility, and clarify requirements

Provided leadership, mentoring, and coaching to more junior team members.

Jan 2020 – Mar 2022 with First Advantage - Atlanta, GA

Generative AL/ML Eng

As Generative AI/ ML Engineer, I developed and improved machine learning models to integrate OCR technology into the existing workflow, resulting in a more streamlined and efficient process for data entry and claims processing. I used OCR technology to extract text from various types of healthcare documents, such as medical records, prescriptions, lab reports, and insurance claims to automate data entry and streamline claims processing

The process of drug discovery is complicated and involves many disciplines as they are often bounded by billions of testing, and huge financial and time expenditures. It takes twelve years on average to get a drug officially submitted. The data science and machine learning algorithms streamline and shorten this process, adding a perspective to each step from the initial screening of drug compounds to the prediction of success rate based on biological factors. Such algorithms can forecast how the compound will act in the body using advanced mathematical modeling and simulations instead of the "lab experiments". The idea behind computational drug discovery is to create computer model simulations as a biologically relevant network simplifying the prediction of future outcomes with high accuracy. It allows choosing which experiments should be done and incorporates all the new information in a continuous learning loop. Analogous techniques are used to predict the side effects of some particular chemical combinations.

Applied advanced analytics skills, with proficiency at integrating and preparing large, varied datasets, and communicating results.

Developed analytical approaches to strategic business decisions.

Performed analysis using predictive modeling, data/text mining, and statistical tools.

Collaborated cross-functionally to arrive at actionable insights.

Synthesized analytic results with business input to drive measurable change.

Assisted in the continual improvement of the AWS data lake environment.

Designed Architecture Diagram for the Feature Store to migrate to AWS

Identifying, gathering, and analyzing complex, multi-dimensional datasets utilizing a variety of tools.

Performed data visualization and developed presentation material utilizing Tableau.

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

Participated in all phases of data mining; data collection, data cleaning, developing models, validation, visualization, and performed Gap analysis.

Coordinated with other teams such as the dashboard team to provide model results, and patient and medication information to populate the dashboard, and data integration tests were also performed

Implemented CI-CD pipeline for deployment and MLOps in MS Azure

Responsible for defining the key business problems to be solved while developing, and maintaining relationships with stakeholders, SMEs, and cross-functional teams.

Used Agile approaches, including Extreme Programming, Test-Driven Development, and Agile Scrum.

Provided knowledge and understanding of current and emerging trends within the analytics industry

Participated in product redesigns and enhancements to know how the changes will be tracked and to suggest product direction based on data patterns.

Applied statistics and organized large datasets of both structured and unstructured data.

Use of algorithms, data structures, and performance optimization.

Worked with applied statistics and/or applied mathematics.

Facilitated the data collection to analyze document data processes, scenarios, and information flow.

Determined data structures and their relationships in supporting business objectives and providing useful data visualizations in reports.

Promoted enterprise-wide business intelliEnce by enabling report access in SAS BI Portal and on Tableau Server.

Packages used: NumPy, Scikit-Learn, Pandas, Matplotlib, Seaborn, Github, PostgreSQL, Shap, a-priori, doc2vec, Keras, Azure DataBricks, MLFlow

Jan 2018 – Jan 2020 with East West Bank, Pasadena, California

As a Machine Learning Engineer - MLops/ Data Scientist

(East West Banking Corporation operates as a commercial bank. The Bank

offers deposits, saving accounts, loans, investments, credit cards, cash management,

and other banking services)

I used a Seq2Seq model to perform complex queries of convoluted tables and dashboards using natural language. My duties involved the creation and implementation of the system on EWB’s internal web.

I worked using Python and SQL. Worked on computer vision-based OCR models

Used Python packages NumPy, Pandas, Seaborn, Pytesseract, OpenCV, and Tensorflow for this computer vision and NLP-based OCR problem

A very large, proprietary feature set was engineered based on the large pool of data collected by East West Bank

Responsible for pulling data from their enormous SQL DataBase using MS SQL for presenting analyses with Tableau.

Developed Apache PySpark modules for retrieving data from the SQL database using PySpark module.

Built a Tree-Based Model with XGBoost which was used to classify cases as either suspicious or safe enabling appropriate, timely action for all suspicious behavior.

Used Python's PyTorch package for building proposed Neural Network models which extrapolated better than the Tree baseline.

Unsupervised Gaussian Mixture Models helped to identify anomalous data points.

Used an ensemble method to combine the results of supervised and unsupervised models.

Model Recall was not allowed below a threshold determined by domain experts.

Worked closely with their Fraud and Cyber Security departments to integrate model outputs into related operations.

Worked with a team of five other specialists and designed Architecture Diagram for the Feature Store to migrate to AWS

I built the model on a cloud notebook platform.

Assisted in handing over the product for deployment in IBM Mainframes with my pickled serialized model.

Performed cross-validation and model selection using k-folds, train-validate-test, and information-theoretic criteria.

Cleaned and prepared raw data by doing Exploratory Data Analysis and Data Visualization using Pandas, Numpy, Seaborn, and matplotlib.

Rapid model creation in Python using Pandas, NumPy, SKLearn, and plotly for data visualization.

Implemented Classification using supervised algorithms like Logistic Regression, Decision trees, KNN, and Naive Bayes.

Jan 2016– Dec 2018 with Alliance Data, Columbus, Ohio

Data Scientist

(Alliance Data is a consumer financial services company. Augmented credit decisions by incorporating deep learning approaches using Cox Proportional Hazard (CPH) with XGBoost backend. Worked on a large complex longitudinal dataset spanning 20 years of data stored in SQL warehouse. Deployed model on a Spark cluster. Our model was able to predict the credit limit and reduce the probability of default).

This project was focused on customer segmentation based on machine learning and statistical modeling effort, including building predictive models and generating data products to support customer segmentation.

Applied analytics concepts of probability, distribution, and statistical inference on given datasets to unearth interesting findings through use of comparison, T-test, F-test, R-squared, P-value etc.

Performed data mining using state-of-the-art methods and executed large efficient SQL queries.

Enhanced data collection procedures to include information relevant for building analytic systems.

Processed, cleaned, and verified the integrity of data used for analysis.

Conducted ad-hoc analysis and presentation of analytics in a clear manner.

Automated anomaly detection systems and constantly tracked performance.

Implemented data architecture and employed data modeling techniques.

Hands-on use of commercial data mining with tools created in R-Programming and Python.

Created machine learning algorithms using Scikit-learn and Pandas.

Built predictive models to forecast risks for credit and loan products.

Developed dashboards for use by executives for ad hoc reporting using the Tableau data visualization tool.

Developed clusters using information from prospect database for enabling marketing initiatives.

Academic Credentials

Master of Science in Applied Data Science, University of Southern California, Los Angeles, CA

Bachelor of Science in Computer Science, University of California, Irvine, CA

Responsibilities

Responsibilities

Responsibilities

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