Vincent Techo

(PhD, MEd, MBA, BSc, PGD-ML)

Data Scientist / AI Engineer

Phone: 612-***-**** Email: adzh28@r.postjobfree.com

Professional Summary.]

A highly skilled and experienced data scientist with a passion for analyzing and modeling complex datasets. I have 10 years of extensive experience in statistical analysis, machine learning, and predictive modeling. With expertise in Python, R, and SQL programming languages, I have successfully implemented data visualization and mining techniques to uncover valuable insights from large-scale data sets. In addition, I possess a solid understanding of cloud computing, Hadoop, Spark, Tableau and Power BI, and have utilized these technologies to perform business intelligence, data warehousing, and time series analysis. I have a strong understanding of data mining, wrangling, and exploratory data analysis (EDA), and have applied these skills to various projects in industries including education, finance, marketing, and healthcare. With a strong foundation in statistics, programming, and machine learning, I am confident in my ability to apply my technical skills to real-world data analysis problems. Throughout my career, I have employed agile methodology and experiment design to drive informed decision-making, while also leveraging my knowledge of A/B testing to optimize performance. With a deep

interest in deep learning, natural language processing, and artificial intelligence, I am eager to contribute my skills and expertise to drive data-driven innovation and growth. With excellent communication and collaboration skills, I am eager to contribute my passion for data science to a team-oriented environment and make a meaningful impact.

Technical Skills

Machine Learning

Classification Algorithm: Logistic Regression, K-NN, Decision Tree, Random Forest, SVM

Regression Algorithms: Linear Regression, Decision Tree.

Clustering Techniques: K-Means, Hierarchical.

Ensemble Techniques: Bagging, Boosting, AdaBoost, Gradient Boost, XGBoost.

Dimensionality Reduction: PCA and LDA

Deep Learning, Generative AI & Deployment

Neural Nets: Tensorflow, Keras, Pytorch, LSTM, CNN.

Model Deployment: AWS EC2, Terraform, Flask, APIs, CI/CD.

Generative AI: NLP, OCR, GPT-4, BERT, PaLM, BARD, KerasCV, Stable Diffusion.

Tools & Languages

Programming Languages: Python, R, SQL, MATLAB, DAX

Data Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn, Plotly, ggplot2

Statistical Tools: SPSS, Excel.

Cloud Computing: Azure, AWS, Vertex AI, GCP.

Statistical Analysis

EDA: Outlier detection, Sampling techniques, Boxplots.

Inferential Analytics: Hypotheses tests, z-test, t-test, A/B test, ANOVA.

Databases

MySQL Server: Advanced SQL.

PostgreSQL: Advanced queries, transactions, indexing.

NoSQL: MongoDB

Others

Data Mining: Data preparation, Model building, Model evaluation, Web scraping.

Big Data Engineering: Hadoop, Apache Spark.

Business Intelligence/Analysis: Dashboards and Reporting using Power BI and Tableau.

Professional Experience

Lead Data Scientist

US Bank, Minneapolis, Minnesota

01/2022 – Present

U.S. Bank National Association is the fifth largest banking institution in the United States. The company provides banking, investment, mortgage, trust, and payment services products to individuals, businesses, governmental entities, and other financial institutions.

Successfully implemented mortgage loss forecasting and handled data analytics and reporting work by extensively using Python, SQL, SPSS, and Excel skills. Performed model validation, code review, and stress testing on loss forecast models. Developed reports using SAS reporting procedures, Python data visualization libraries, and Tableau/ Power BI / Excel dashboards. Assisted in model outputs analysis and interpretation.

•Led a cross-functional team of data scientists and engineers in the end-to-end development and implementation of a cutting-edge AI-based fraud detection system. The system successfully identified and prevented a significant number of fraudulent transactions, resulting in substantial financial savings for the organization.

•Developed and implemented churn prediction models using advanced machine learning algorithms to accurately identify customers at risk of attrition.

•Deployed the models on AWS Sagemaker. Setup API gateway and test END Point.

•Conducted market segmentation analysis to identify distinct customer segments and develop targeted marketing strategies for each segment.

•Utilized customer lifetime value estimation techniques to quantify the long-term value of customers and inform strategic decision-making.

•Collaborated with cross-functional teams to define project objectives, gather data requirements, and develop analytical solutions.

•Extracted insights from large datasets using statistical analysis.

•Developed and deployed sophisticated anomaly detection algorithms to effectively identify and flag aberrant patterns in customer behavior, resulting in a significant reduction in false positive alerts.

•Implemented advanced machine learning algorithms, such as random forests and gradient boosting, to optimize customer segmentation and personalize marketing campaigns.

•Provided technical guidance and mentorship to junior data scientists, fostering a collaborative and innovative team environment.

•Incorporated diverse external data sources, including social media platforms and web scraping, to enrich customer profiling and enhance the accuracy of predictive models.

•Developed and maintained data pipelines and ETL processes to ensure data quality and availability for AI initiatives.

•Developed and deployed a generative AI-based customer support system that leveraged natural language processing techniques to automate responses to frequently asked customer queries.

•Collaborated with IT teams to deploy machine learning models into production, ensuring scalability and real-time performance.

•Designed and implemented a machine learning-based customer lifetime value prediction model, empowering the marketing team to make data-driven decisions for optimizing customer acquisition and retention strategies.

•Deployed, managed, and scaled the containerized model using Azure Kubernetes Service (AKS) with load balancers to ensure efficient traffic distribution and high availability.

•Used Azure Kubernetes Service (AKS) and Containers assistance in deployment, scalability, load balancers, version controls and DevOps integration.

Senior AI Engineer

Rockwell Automation, Milwaukee, WI

11/2020 - 01/2022

Led the development and execution of advanced data analytics and machine learning initiatives. Led cross-functional teams to deliver data-driven solutions that drive business growth and innovation. Worked within the Data Analytics team as a Machine Learning Engineer to develop data pipelines, MLOps pipelines, OCR models, reports, and data validations. Communicated complex technical concepts and analysis results to both technical and non-technical audiences. Provided mentorship and guidance to junior team members, shared knowledge, and promoted a culture of learning and continuous improvement. Collaborated with business stakeholders to identify opportunities to leverage data to drive better decision-making and improve business outcomes.

•Worked on computer vision-based OCR models.

•Implemented CNN-based Tesseract with BERT for named entity recognition.

•Utilized Python, Pytesseract, OpenCV, and TensorFlow for this computer vision and NLP-based OCR problem.

•Deployed the chatbot on the Google Cloud Platform (GCP), and utilized various services such as BigQuery, Dialogflow, Kubernetes, and the Container Engine.

•Created and executed comprehensive test scenarios and plans to ensure the reliability and performance of developed models and applications.

•Developed and implemented machine learning algorithms for enterprise-wide AI applications, resulting in improved customer experiences and increased revenue generation.

•Built and supported a community of Citizen Data Scientists within the organization, organizing workshops and training sessions to promote data literacy.

•Conducted data mining and analysis to identify patterns and trends, enabling predictive modeling for business outcomes optimization.

•Analyzed system efficiency and identified bottlenecks, proposing automation opportunities to improve productivity and sustainability.

•Built and supported a community of Citizen Data Scientists within the organization, organizing workshops and training sessions to promote data literacy.

•Conducted data mining and analysis to identify patterns and trends, enabling predictive modeling for business outcomes optimization.

•Led the deployment of AI/ML models from development to production, ensuring rigorous validation and testing protocols were followed.

•Assisted in the development of IoT and Connected Enterprise projects, leveraging data insights to drive innovation and operational efficiency.

•Collaborated with cross-functional teams, including data scientists, engineers, and business units, to align data projects with organizational objectives.

•Troubleshoot performance issues and refined models based on feedback and outcomes, resulting in enhanced accuracy and efficiency.

Lead Data Scientist

Planet Fitness, Hampton, New Hampshire.

11/2018 - 10/2020

Planet Fitness is an American franchisor and operator of fitness centers based in Hampton, New Hampshire. The company reports that it has around 2,400 clubs, making it one of the largest fitness club franchises by number of members and locations. Explored the company dataset with Python and came up with observations and business insights from the data. Built a customer profile to help capitalize based on it and help the marketing department to target customers. Extracted actionable insights that drive the sales of the business.

•Acted as a Data Scientist in a highly technical and analytical role, harnessing a passion for data, mathematics, programming, and statistics.

•Mined, aggregated, and analyzed data to provide predictive insights and outcomes for key drivers of the business.

•Collaborated with business stakeholders, utilizing analytics to drive change and improvement.

•Identified opportunities for organic growth, new store site selection, operational efficiencies, marketing strategies, and overall profitability.

•Cleaned text data using various techniques to ensure its quality and suitability for analysis. and involved in implementing methods such as text preprocessing, removal of noise and irrelevant information, and handling missing data.

•Gained insights from the text data, conducted exploratory data analysis (EDA) using techniques like Bag of Words, K-means clustering, and DBSCAN and identified patterns, grouped similar data points, and extracted meaningful information from the text corpus.

•Identified and experimented with different embedding techniques including Universal Google Encoder, DocToVec, TF-IDF, BERT, and ELMO .

•Evaluated the performance of each embedder, determined the optimal choice that yielded the best results in terms of matching user inputs with trained questions, and associated them with the corresponding department.

•Developed quality reporting tools to extract data from multiple sources, providing proactive trend analysis and expected business outcomes.

•Gained a deep understanding of operational processes and needs, using technology to deliver impactful solutions.

•Established protocols, methods, and systems to collect, aggregate, store, and analyze data.

•Designed, developed, and maintained business intelligence software/platforms.

•Created ad hoc reporting and analysis to support the organizational hierarchy.

•Constructed dashboards and other visualization tools for easy data consumption by stakeholders and end-users.

•Investigated market demographics and competitor landscape, suggesting offensive/defensive marketing tactics.

•Utilized Geographic Information System (GIS) technology to evaluate member plotting, drive time reach, and market penetration potential.

•Provided predictive analysis for membership and new unit forecasting, and supported club acquisition underwriting and market growth strategy.

•Identified revenue-capturing opportunities, cost-control methods & profitability drivers.

•Measured marketing campaign effectiveness (ROI).

•Leveraged business intelligence technologies such as Microsoft Power BI, Tableau, GIS, SAS, SQL, and Python, and demonstrated exceptional use of Excel and the Microsoft Office Suite.

Data Scientist

Thrasio, Walpole, Massachusetts

12/2015 – 11/2018

Thrasio is an Amazon aggregator Start-up that acquires third-party merchants to manage and grow brands in either the Amazon marketplace and/or various direct-to-Consumer strategies

Built a pipeline for the Supply Chain team to produce weekly Inventory Reconciliation Reports, which included Inventory Mismatch Reporting, Missing Shipments, and Transfer Order completeness.

•Worked with Finance to troubleshoot and enhance Settlement Payment Reports for cash reconciliation.

•Built pipeline to map SKUs to corresponding UPC/EAN.

•Validated data for MWS to SPAPI transition.

•Collaborated with cross-functional teams of data scientists, user researchers, product managers, designers, and engineers passionate about our consumer experience across platforms and partners.

•Performed analyses on large sets of data to extract impactful insights on user behavior that helped drive product and design decisions.

•Worked with the Python package Pandas and Feature Tools for data analytics, cleaning, and model feature engineering.

•Updated Python scripts to match training data with our database stored in AWS Cloud Search so that we could assign each document a response label for further classification.

•Performed Supply Chain Reporting analytics and ran in Power BI.

•Built dashboards in Periscope for internal usage and reporting.

•Extracted source data from Amazon Redshift on the AWS cloud platform.

•Built, trained, and deployed machine learning models using Amazon Sage Maker.

•Designed and implemented a CI/CD pipeline to automate model development and model deployment.

•Setup model training pipelines to be triggered when model drift is detected.

•Utilized AWS Sage Maker ML Ops tools.

Data Scientist

Amway, Ada Township, Michigan

04/2013 – 11/2015

Created a data analytics project for Amway, a multi-level marketing company specializing in health, beauty, and home care products, can provide valuable insights and help optimize various aspects of the business.

•Gathered data from various sources, including sales transactions, customer interactions, product inventory, and marketing campaigns.

•Conducted EDA to gain an initial understanding of the data. Identify data quality issues and perform data cleansing, if necessary.

•Created data visualizations and summary statistics to explore trends and patterns.

•Segmented Amway's customer base to understand different customer profiles.

•Analyze customer behavior, purchase history, and demographics.

•Built predictive models to forecast sales for Amway products.

•Used time-series analysis and machine learning techniques to predict future sales trends.

•Developed a recommendation engine that suggests relevant products to customers based on their purchase history and preferences.

•Implemented personalization techniques to enhance the shopping experience

Education

Postgraduate Degree in Artificial Intelligence and Machine Learning

University of Texas, McCombs School of Business

Master's Degree in Instructional Technology

University of Maryland Global Campus (UMGC)

Certifications & Licenses

•Programming Certifications - Beginner & Advanced

•IBM Applied Data Science Certification - Coursera

•IBM Data Science and Artificial Intelligence Certification - LearnBay

•Data Science and Artificial Intelligence Certification - Intellipaat

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