Functional Skills
Accounting
Machine Learning
Agile Software Development
Data Science
Predictive Modeling
Customer Analytics
Operational Efficiency
Business Analysis
Business Intelligence
Data Analysis
Data Management
Financial Management
Financial Modeling
Project Management
Strategic Planning
Software Skills
Airflow
Docker
Java
R
Microsoft PowerPoint
Machine Learning
C
Python
SQL
TensorFlow
PySpark
Go
Query optimization
TypeScript
Vectorization
Sector Experience
Consumer Goods
Financial Services
Media & Entertainment
Languages
English
Notable Clients
Nestle Purina
Big 4 Accounting
Bulge Bracket Investment Banks
Fortune 500
Top Consulting Firms
Experience
Osheiza Otori LLC
Data Science / Analytics
Owner
2/2023 - Present
As the owner of my data science consultancy, I have helped clients drive multimillion-dollar business impact by delivering data-driven solutions using AI, machine learning, and advanced analytics.
Indigeno
Data Science / Analytics
Quantitative Analyst
10/2024 - Present
• Analyzed large-scale user engagement data for Microsoft Ignite 2024 and Microsoft Build 2025, delivering 15 strategic recommendations to 25+ senior executives for future conference optimization
PricewaterhouseCoopers (PwC)
Engineering
Senior Python Developer
11/2024 - Present
• Reduced runtime by 92% (18+ hours to 1.5 hours) for credit card forecasting across entire portfolio by translating SAS-based model to Python with NumPy vectorization and optimized SQL queries
• Integrated enterprise VaR models and credit forecasting systems with Java and Python for 2 major banking clients
• Integrated enterprise VaR models and credit forecasting systems with Java and Python for 2 major banking clients
Suited
Data Science / Analytics
Data Analyst
4/2024 - 2/2025
• Generated $400K in new revenue through gradient boosting regression models (XGBoost) analyzing assessment-performance correlation using feature engineering on employee performance metrics, achieving 0.85 R² score with cross-validation
• Improved reporting efficiency 5X via automated ETL pipeline using Google Cloud Functions with Python, Cloud Storage, and BigQuery integration
• Expanded data accessibility across organization through collaborative Tableau dashboard development with SQL query optimization
• Improved reporting efficiency 5X via automated ETL pipeline using Google Cloud Functions with Python, Cloud Storage, and BigQuery integration
• Expanded data accessibility across organization through collaborative Tableau dashboard development with SQL query optimization
Cooper AI
Data Science / Analytics
Senior Data Scientist
10/2024 - 2/2025
• Identified multimillion-dollar EBITDA opportunities for Texas dental clinic network through ensemble machine learning models (XGBoost, Random Forest) analyzing operational efficiency metrics, patient flow optimization using time-series forecasting, and revenue prediction via regression analysis on historical appointment data
Bank of America
Data Science / Analytics
Sr. Data Analyst
2/2023 - 9/2024
• Prevented $15M loss by implementing statistical anomaly detection algorithms in Python to identify performance degradation in Erica virtual assistant and worked with platform engineering team to remediate within one release cycle
• Reduced attrition 5% QoQ by building logistic regression churn prediction models to analyze customer interaction patterns, delivering actionable insights via SQL-based customer segmentation and cohort analysis
• Achieved 98% accuracy for Erica virtual assistant, creating 5 new user experiences based on behavioral analysis
• Eliminated 80% of reporting overhead by developing automated Python ETL pipelines and a Python script that automated PowerPoint presentation served to C-suite stakeholders
• Reduced attrition 5% QoQ by building logistic regression churn prediction models to analyze customer interaction patterns, delivering actionable insights via SQL-based customer segmentation and cohort analysis
• Achieved 98% accuracy for Erica virtual assistant, creating 5 new user experiences based on behavioral analysis
• Eliminated 80% of reporting overhead by developing automated Python ETL pipelines and a Python script that automated PowerPoint presentation served to C-suite stakeholders
Nestlé purina
Engineering
Sr. PlatformEngineer
7/2023 - 12/2023
• Accelerated experimentation 75% (8 weeks to 2 weeks) through LaunchDarkly feature flag architecture with NodeJS/TypeScript integration across React/Gatsby codebases
• Increased conversion rates 5% by designing and executing 3 A/B experiments per week across brand properties
• Boosted reward redemptions 4% MoM using collaborative filtering algorithms to analyze purchase history patterns
• Increased conversion rates 5% by designing and executing 3 A/B experiments per week across brand properties
• Boosted reward redemptions 4% MoM using collaborative filtering algorithms to analyze purchase history patterns
Digits Financial, Inc
Data Science / Analytics
Analytics Lead
7/2022 - 1/2023
out the analytics function at Digits.
• Cut abandonment rate 30% through customer journey funnel analysis using event tracking data (Segment), implementing cohort retention analysis with SQL window functions, and A/B testing optimization strategies with statistical significance testing
• Improved customer health 10% WoW via automated scoring system enabling proactive intervention
• Developed 50+ data models using dbt with comprehensive testing framework, serving as company-wide analytics foundation
• Cut abandonment rate 30% through customer journey funnel analysis using event tracking data (Segment), implementing cohort retention analysis with SQL window functions, and A/B testing optimization strategies with statistical significance testing
• Improved customer health 10% WoW via automated scoring system enabling proactive intervention
• Developed 50+ data models using dbt with comprehensive testing framework, serving as company-wide analytics foundation
Subject Technologies, Inc.
Data Science / Analytics
Senior Data Science Manager
1/2022 - 5/2022
EdTech company with a mission to make education accessible for all. As the first Data hire, my main purpose was to create a business-driven data culture at Subject, while building a collaborative environment within my team, dedicated to excellence and learning.
• Built and led team of 3 data scientists while implementing agile methodologies, code review processes with Git workflows, and technical mentorship programs across junior to senior levels
• Doubled contract acquisition through unsupervised K-means clustering analysis on customer behavioral data, feature engineering with scikit-learn preprocessing, and predictive lead scoring models using random forest algorithms with hyperparameter tuning
• Saved 20 hours/week for Customer Success team through automated ticket enrichment system using FactBranch API integration and customer context aggregation via SQL joins across multiple data sources
• Increased course completion 20% by implementing survival analysis on student engagement
• Built and led team of 3 data scientists while implementing agile methodologies, code review processes with Git workflows, and technical mentorship programs across junior to senior levels
• Doubled contract acquisition through unsupervised K-means clustering analysis on customer behavioral data, feature engineering with scikit-learn preprocessing, and predictive lead scoring models using random forest algorithms with hyperparameter tuning
• Saved 20 hours/week for Customer Success team through automated ticket enrichment system using FactBranch API integration and customer context aggregation via SQL joins across multiple data sources
• Increased course completion 20% by implementing survival analysis on student engagement
Seriously Digital Entertainment Inc.
Data Science / Analytics
Data Scientist
4/2019 - 1/2022
Entertainment company with a passion for great storytelling. As a data scientist, I worked closely with both the Marketing and Product teams to identify, quantify, and validate growth strategies, predict player tendencies and spending patterns, and measure overall engagement/enjoyment.
• Increased in-app revenue 3% and ARPU 8% through behavioral pattern recognition using time-series clustering (LSTM networks), A/B testing with multi-armed bandit algorithms, and player segmentation via hierarchical clustering
• Achieved 100% D180 ROAS on 90% of marketing channels with a total of $200M budget through customer lifetime value prediction using survival analysis and attribution modeling
• Reduced D30 churn by 5% by developing a productionalized random forest ensemble model (87% AUC) using feature engineering on player interaction data
• Improved game ratings 25% through NLP sentiment analysis on chat data using transformer models (BERT), topic modeling with Latent Dirichlet Allocation, a
• Increased in-app revenue 3% and ARPU 8% through behavioral pattern recognition using time-series clustering (LSTM networks), A/B testing with multi-armed bandit algorithms, and player segmentation via hierarchical clustering
• Achieved 100% D180 ROAS on 90% of marketing channels with a total of $200M budget through customer lifetime value prediction using survival analysis and attribution modeling
• Reduced D30 churn by 5% by developing a productionalized random forest ensemble model (87% AUC) using feature engineering on player interaction data
• Improved game ratings 25% through NLP sentiment analysis on chat data using transformer models (BERT), topic modeling with Latent Dirichlet Allocation, a