DAMOPE AYORINDE
Dallas,Texas
Summary
Meticulous Data Scientist accomplished in compiling, transforming and analyzing complex information through software. Expert in machine learning and large dataset management. Demonstrated success in identifying relationships and building solutions to business problems. Translating complex business challenges into actionable data-driven insights and communicating complex technical concepts to non-technical stakeholders, underscored by a passion for staying abreast of industry trends and emerging technologies to drive innovation and maintain a competitive edge consistently.
Overview
8
8
years of professional experience
1
1
Certification
Work History
Data Scientist | Machine Learning Engineer
AMERICAN AIRLINES
10.2020 - Current
- Orchestrated groundbreaking 15% reduction in fleet maintenance downtime by implementing cutting-edge machine learning algorithms to optimize schedules
- Pioneered revolutionary Natural Language Processing (NLP) techniques, transforming customer feedback analysis and refining experience strategies at executive level.
- Created analytical insights and dashboards using Power BI which enabled 10% increase in enterprise account conversions over quarter for support product.
- Engineered and deployed predictive models that streamlined operations, contributing pivotal insights for demand forecasting, crew scheduling optimization, and resource allocation
- Experience in predictive analytics procedures analyzed in supervised learning(Classification, Regression, Decision Trees,Random Forest, SVM, Neural Networks), unsupervised learning (Clustering K - Means & PCA), and reinforcement learning
- Developed and maintained sophisticated Power BI dashboards, providing executive-level insights into key performance indicators for strategic decision-making
- Spearheaded cross-functional collaboration to extract salient features for optimization modeling, ensuring data-driven decision-making at highest level
- Established end-to-end APIs using Rancher & APIGEE, delivering real-time destination recommendations on AA.COM, elevating customer experience
- Develop interactive dashboard using tools like Tableau or Power BI to present visualizations of sentiment distribution, topic modeling results, and key entities
- Developed and implemented NLP models for sentiment analysis, achieving 15% improvement in accuracy over existing models
- Created dynamic Power BI dashboards for stakeholders to interactively explore customer segments
- Break down larger Machine Learning initiatives into pieces that deliver incremental business value and guide team through implementing them
- Act as bridge between technical and non-technical stakeholders, facilitating effective communication and understanding of project goals and outcomes
- Full stack experience in data collection, aggregation, analysis, visualization, , and monitoring of data science products
- Develop reports using SQL and Python (Pandas, Matplotlib, Excel)
- Employed Keras and TensorFlow to build deep learning models for predicting equipment failures
- Utilized Azure Machine Learning services for model training, evaluation, and deployment
- Led design and implementation of interactive dashboards using Tableau and Power BI, providing stakeholders with real-time insights into key performance indicators
- Developed and deployed machine learning models for predictive analytics, resulting in 25% improvement in demand forecasting accuracy
- Achieved accuracy rate of 85% through use of advanced machine learning models
- Conducted A/B testing and performance evaluation of different search configurations to optimize search results
- Write scripts to automate data processing and access of data on AWS (Amazon Web Services) cloud process
- Developed and deployed RAG Chat bot using Lallma-2 13billion parameters with features such as contextual summarization
- Utilized Pandas for data preprocessing and feature engineering, ensuring models were trained on relevant and accurate data
- Engineered end-to-end data pipelines using Python, integrating with cloud platforms (AWS, Azure) for scalable and efficient data processing.
Data Scientist | NLP Engineer
ABBOTT
06.2019 - 10.2020
- Engineered predictive models with machine learning algorithms, resulting in substantial 10% reduction in Abbott's product inventory costs, showcasing executive-level impact
- Execute ETL processes in order to assess client datasets, identify nulls or errors, and load to appropriate platforms
- Led implementation of NLP algorithms for comprehensive customer feedback analysis, gaining nuanced insights into product performance for executive decision-makers
- Collaborate with business partners to develop innovative solutions to meet objectives utilizing cutting edge techniques and tools
- Utilized Matplotlib for visualizing customer segments and presenting insights to marketing teams
- Developed and implemented machine learning models to optimize search relevance and query understanding within organization's knowledge management system, leveraging Amazon Kendra
- Designed and deployed Tableau dashboards, offering executive-level visualization of key indicators crucial for strategic decision-making
- Trained multi-language translation system using T5 base as base model
- Migrated data into ADF and then, converted Python code to Pyspark to ensure compatibility in ADB (Azure DataBricks)
- Fine-tuned TTS system using tacotron2 as base model (Transformer and LSTM architecture)
- Led integration of Amazon Kendra as primary search solution, resulting in 25% improvement in search accuracy and 30% reduction in query response time
- Utilized SQL for data extraction, transformation, and loading (ETL) tasks, optimizing data processing efficiency
- Implemented real-time monitoring system using Scikit-learn to trigger maintenance alerts based on model predictions
- Utilized SQL for efficient data retrieval and preprocessing tasks, ensuring data readiness for visualization
- Conducted thorough exploratory data analysis (EDA) to uncover trends in healthcare data, contributing to executive-level decision-making for improved patient outcomes
- Developed personalized recommendation engines using collaborative filtering techniques in TensorFlow and Keras
- Built and monitored Hybrid Recommender System for Abbott retail, achieving cross-selling and upselling objectives and showcasing executive-level innovation
- Utilized PuLP and CVXPY to implement and solve optimization models, ensuring executive-level scalability, efficiency, and accuracy
- Assisted in development of Tableau dashboards for executive-level reporting, providing insights into sales performance
- Developed neural network-based predictive maintenance model in Python, reducing downtime by 25%
- Designed and maintained data pipelines on both Azure and AWS, ensuring data integrity and accessibility
- Utilized Azure Machine Learning and AWS SageMaker for model training, evaluation, and deployment
- Applied K-means clustering with Scikit-learn to segment customers based on their behavior and preferences
- Conducted A/B testing to evaluate effectiveness of marketing campaigns, leading to 15% improvement in conversion rates
- Designed service request dashboard using Power BI, achieving substantial 60% reduction in SLA breach, showcasing executive-level impact.
Data Scientist
WELLS FARGO
01.2016 - 05.2019
- Conceptualized groundbreaking Proof of Concept (PoC) of NLP-based insider trading models, reducing false positives by exceptional 85% and demonstrating executive-level innovation
- Automated regulatory reporting for failed online transactions, slashing man-hours by 90%, ensuring swift compliance, and showcasing executive-level efficiency
- Used unsupervised (K-means, DBSCAN) and supervised learning techniques (Regression, Classification) for feature engineering and did Principal Component Analysis for dimensionality reduction of features
- Reduced downtime by 20% and maintenance costs by 15%, leading to significant operational efficiency
- Conducted advanced analytics using Python, integrating SQL queries to uncover trends and patterns in large datasets
- Partnered with stakeholders to gather requirements, develop roadmaps, and implement process improvements, enhancing executive-level project lifecycle functionality by 37%
- Facilitated migration of data using T-SQL and implemented related machine learning models and migrated to Azure Cloud thereby leading to reduction in server cost by 16%
- Designed interactive dashboards using Plotly, providing stakeholders with real-time insights into key performance indicators
- Collaborated with data scientists, analysts, and business stakeholders to understand data requirements and visualization needs
- Collaborated with cross-functional teams to understand business requirements, leading to development of data-driven solutions Utilized statistical analysis techniques in Python to uncover trends and patterns in large datasets, informing strategic business decisions.
Education
Bachelor of Science - Computer Science
University of North Texas
Denton, TX12.2017
Skills
- MySQL
- Tableau
- EDA/Dashboard Development
- Microsoft Power BI
- Python
- Oracle
- Microsoft Excel
- HTML
- CSS
- Docker
- Azure
- PuLP
- CVXPY
- SQL
- Tensor Flow
- Scikit-Learn
- PyTorch
- Jupyter Notebooks
- Matplotlib
- Seaborn
- Git
- R
- Apache Hadoop
- Spark
- Keras
- Pandas
- NumPy
- AWS
- SAS
- MongoDB
- NoSQL Database
- RapidMiner
- Data Science Platform
- Advanced Regression Analysis
- Ensemble Learning Techniques
- Time Series Forecasting
- Bayesian Statistics
- Unsupervised Learning
- Feature Selection and Engineering
- R Programming
- Data Visualization
- Natural Language Processing
- Deep Learning Architectures
- SQL and Database Management
- Effective Data Storytelling
- Stakeholder Alignment
- Business Strategy Integration
- Complex Problem Solving
- Experimental Design and Hypothesis Testing
- Agile Methodology Integration
- Statistical Analysis
- Data repositories
- Data Governance
- Machine Learning
- Analytical Thinking
- Advanced data mining
- Database Management
- Agile Methodology
- Sentiment Analysis
- Neural Networks
- Python Programming
- SQL Databases
- Amazon Redshift
- Project Planning
- Problem-Solving
- Organizational Skills
- Software Proficiency
- Flexible and Adaptable
- Remote Office Availability
- Data programming
- Anomaly Detection
Clearance
Public Trust
Education Certifications
- UNIVERSITY OF NORTH TEXAS, Denton, Denton, Texas, Bachelor in Computer Science
- Oracle Autonomous Database Cloud Certified Specialist
- Applied Machine Learning / AI
- Azure Engineer Expert
- Tableau Desktop Certified Associate
- Microsoft Certified Professional – MTA
Certification
Oracle Autonomous Database Cloud Certified
Specialist
Applied Machine Learning / AI
Azure Engineer Expert
Tableau Desktop Certified Associate
Microsoft Certified Professional – MTA
Timeline
Data Scientist | Machine Learning Engineer
AMERICAN AIRLINES
10.2020 - Current
Data Scientist | NLP Engineer
ABBOTT
06.2019 - 10.2020
Data Scientist
WELLS FARGO
01.2016 - 05.2019
Bachelor of Science - Computer Science
University of North Texas