Verified Expert in Engineering
Data Scientist and Developer
Daniel is a data scientist who builds predictive models, data visualizations, and dashboards on large datasets with expertise in the staffing and investment management fields. He made a graph neural network to predict risk on a construction site reducing errors by 60%. Daniel improved a predictive modeling framework, handling millions of records on policyholder behavior using R and H2O. His models are used in financial forecasts and dashboards to catch aberrant behavior as it unfolds.
Docker, Julia, R, Python
The most amazing...
...project I have completed is predicting construction site risk. It used custom text embeddings and GNNs to greatly improve performance.
Backyard Eats LLC.
- Created a layout algorithm that provided optimal configurations given a provided garden layout and order.
- Developed Excel user interface to allow for easy input/output control, as well as user interaction with the result.
- Architected automated visualization for the final result using Plotly.
Zoetis - Main
- Transitioned and streamlined data process from Microsoft SQL Server to Databricks. Automated dozens of manual processes to increase efficiency and reduce error.
- Built multiple performance dashboards covering large subsections of Zoetis' customer population to inform the leadership of the performance of those subsections.
- Built analytics informing pricing for a key product whose patent was expiring. This included developing customer risk analytics, product cannibalization analytics, and competitive price and price elasticity models, which had not been done before.
- Collaborated with Sales and Marketing to provide supporting data for dozens of pricing memos/promotions and contracts.
- Developed detailed data process for measuring drivers of sales allowances from gross to net sales. Involved extensive data discovery and combination of data from multiple sources. Validated detailed data against multiple groups' existing reports.
- Built a graph neural network to predict risk on a construction site. It reduced error relative to the prior model by 60% and provided reasonable results when tested on individual observations that differed from training.
- Delivered a resource management tool that simulated project wins and losses and assessed the risk of understaffing. R Shiny was used to give users an interface to run the process and explore simulation results.
- Conducted data discovery for a healthcare client to assess the state of their data and explore insights that could be extracted with deep learning for drug development. This led to a monthly retainer and future work as data came in.
Senior Actuarial Consultant
- Overhauled four major assumptions to use a predictive modeling framework that could efficiently handle the millions of records on policyholder behavior that went into premises and was much more accurate than the prior approach. Used R and H2O.
- Replaced a compression process (clustering to reduce time with Monte Carlo simulations) with a modern custom solution that reduced cloud costs by 1/3 and improved the accuracy of the simulation.
- Built an investment management tool that calculated the efficient frontier for investments given unique life insurance portfolio constraints.
- Investigated optimal mapping of mutual funds to relevant major indices and built a process to identify which holdings were responsible for inaccuracies in that mapping.
- Implemented a novel method of setting prudency in assumptions to reflect the appropriate level of conservatism when setting reserves.
- Assisted assumption reviews for several M&A projects.
- Built efficient reporting frameworks for financial simulations, policyholder behavior, and call center data.
Personal Investment Managementhttps://github.com/DDoyle1066/InvestmentManagement
1. Gathering returns from AlphaVantage based on requested tickers (currently Vanguard ETFs and a high yield mutual fund).
2. Estimating the relationship between bond yields and bond funds.
3. Generating a process to project variations in bond yields and ETF returns if bond yields are relevant.
4. Simulating future bond yields and ETF movements based on them.
5. Determining the efficient frontier for that simulation.
6. Repeating #4 and #5 1,000 times to avoid overallocation into a single high-performing fund at the end of the efficient frontier.
Variable Annuity Metamodelinghttps://github.com/DDoyle1066/VA_Metamodeling
1. Generating policyholder in force files.
2. Pulling historical market data and estimating market parameters from historical data. A regime-switching lognormal model is shown as an improvement over a simple lognormal model.
3. Monte Carlo calculation.
4. Approximation of projection using a neural network to predict the output without running the full-scale Monte Carlo projection.
Julia, R, Python, Excel VBA, SQL, C++
RStudio, H2O Deep Learning Platform, Docker, Jupyter Notebook, Databricks
Life Insurance, Predictive Modeling, Probability Theory, Data Visualization, Simulations, Artificial Intelligence (AI), Linear Optimization, Data Analysis, Data Analytics, Predictive Analytics, Finance, Financial Data Analytics, Financial Data, Dashboards, Statistical Modeling, Statistics, Risk Models, Tail Risk, Financial Modeling, Financial Mathematics, Deep Learning, Machine Learning, Neural Networks, Clustering, Optimization Algorithms, Genetic Algorithms, Bayesian Statistics, Healthcare IT, Principal Component Analysis (PCA), Multivariate Statistical Modeling, Linear Regression, Monte Carlo Simulations, Azure Databricks, Risk, Pricing, Reinforcement Learning, Deep Reinforcement Learning, APIs, Layout
Pandas, TensorFlow, NumPy, Matplotlib, PySpark
Git, Jupyter, Seaborn, Tableau, Excel 2013, Plotly
Bachelor's Degree in Actuarial Science
Robert Morris University - Moon Township, Pennsylvania, United States
Associate of the Society of Actuaries
Society of Actuaries