CV | Babs Olaniyi

Summary

Senior Data Scientist and Machine Learning Engineer with 7+ years of experience building and deploying applied machine learning systems across healthcare, e-commerce, supply chain, and commercial analytics. Current work at Causal Foundry focuses on healthcare AI, claims analytics, health financing, provider performance, anomaly detection, and decision-support systems.

Experience

Built claims-driven healthcare financing models used in policy decision-making, including work supporting modeled liquidity for health centers covering 9M insured individuals.
Designed analytics and decision-support workflows for provider performance, healthcare utilization, patient outcomes, and national health financing reform.
Productionized a national drug catalog by reconciling 5,000+ SKUs across seven agencies using text similarity and embedding-based matching.
Developed monitoring systems with anomaly detection and automated indicators to reduce manual reporting and improve decision turnaround across 1,000+ providers.

Designed price-arbitrage and anomaly detection systems that identified pricing inconsistencies and protected approximately EUR11M annually.
Built elasticity and uplift models across a EUR500M+ product portfolio, contributing to a 13% gross-profit improvement.

Forecasted demand for pharmacy networks using time-series and deep learning approaches, reducing stockouts and improving operational monitoring.
Improved mobile health engagement using survival models, churn prediction, return/purchase models, and experiments around user actions.
Designed adaptive intervention and recommendation systems using multi-armed bandits, rule-based optimization, and reinforcement-learning concepts.
Mentored data scientists, supported recruitment, and translated technical results for product and policy stakeholders.

Built customer segmentation and churn models to improve communication, retention, and customer satisfaction.
Combined multiple data sources into revenue, budget, expense, and KPI dashboards that reduced manual reporting.
Analyzed acquisition channels, customer journeys, pricing scenarios, and business performance for management decisions.

Skills

Programming and data

Python, SQL, PySpark, Scala, R, Git, Microsoft Excel

Machine learning

Forecasting, pricing optimization, recommender systems, A/B testing, survival analysis, causal inference, reinforcement learning, anomaly detection, NLP, multimodal learning

Platforms

Google Cloud, BigQuery, AWS, Azure Databricks, Spark, MLflow, Airflow, Tableau, Power BI

Libraries

PyTorch, TensorFlow, Keras, Scikit-learn, XGBoost, LightGBM, pandas, NumPy

Education

MSc Quantitative Economics

University of Paris 1 Pantheon-Sorbonne and Autonomous University of Barcelona, 2017 - 2019. Erasmus Mundus scholarship recipient.

BSc Mathematics and Statistics

Kwara State University, 2012 - 2016. First Class Honors.

Awards and Affiliations

Cloud Credit Award, ML Collective and Google
Google Africa Developer Scholarship Program recipient
Erasmus Mundus scholarship recipient
Best graduating student, School of Statistics and Mathematical Sciences
Member: ASA, ACM, and SIAM

Publications

2025

Enhancing Product Recommendations with Multi-Modal LLMs

ISIR-eCom 2025 work on multimodal product recommendation using text and image signals.

2025

Data-Driven Approach to Capitation Reform in Rwanda

Report on claims-data-driven capitation design, calibration, monitoring, and quality insights for Rwanda's Community-Based Health Insurance scheme.

2022

User Engagement in Mobile Health Applications

KDD 2022 work on probabilistic and survival analysis for meaningful engagement and churn in mobile health applications.

2023

Power Samade distribution: properties and application to real lifetime data

Nigerian Journal of Science and Environment.

2018

Homework vs. In Class-Exercise: Means of Assessment, Waste of Time or Punishment?

International Journal of Scientific and Engineering Research.

CV

Babs Olaniyi