Daniel Doyle, Developer in Pittsburgh, PA, United States
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Daniel Doyle

Verified Expert  in Engineering

Data Scientist and Developer

Pittsburgh, PA, United States

Toptal member since May 3, 2022

Bio

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.

Portfolio

Backyard Eats LLC.
Artificial Intelligence, Optimization Algorithms, Layout, Excel VBA, Julia...
Zoetis - Main
SQL, Tableau Development, Data Analysis, Data Science, R, Bayesian Statistics...
Visimo
Julia, R, Python, Docker, Neo4j, Data Visualization, Simulations, Deep Learning...

Experience

Availability

Full-time

Preferred Environment

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.

Work Experience

AI Developer

2023 - 2024
Backyard Eats LLC.
  • Created a layout algorithm that provided optimal configurations based on the garden layout and order.
  • Developed Excel user interface to allow for easy input/output control and user interaction with the result.
  • Architected automated visualization for the final result using Plotly.
Technologies: Artificial Intelligence, Optimization Algorithms, Layout, Excel VBA, Julia, Plotly, Excel Macros, Excel Development, Visual Basic

Data Analyst

2022 - 2023
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.
Technologies: SQL, Tableau Development, Data Analysis, Data Science, R, Bayesian Statistics, Python, PySpark, Azure Databricks, Databricks, Risk, Pricing, Excel VBA, Excel Development, Linear Regression, Git, Principal Component Analysis (PCA), Predictive Analytics, Financial Data, Dashboard, Statistical Modeling, Data Science, Database, Regression Modeling, Jupyter, Trend Analysis, Excel Macros, Excel Development, Microsoft Outlook, Visual Basic, Data Interpretation

Data Scientist

2021 - 2022
Visimo
  • 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.
Technologies: Julia, R, Python, Docker, Neo4j, Data Visualization, Simulations, Deep Learning, Predictive Modeling, Statistics, SQL, Machine Learning, Data Science, RStudio, RStudio Shiny, Healthcare IT, Artificial Intelligence, Linear Optimization, Neural Network, Pandas, Clustering, Data Analysis, Data Science, Linear Regression, Git, Principal Component Analysis (PCA), Optimization Algorithms, Predictive Analytics, APIs, Dashboard, Statistical Modeling, Data Science, Database, Regression Modeling, Jupyter, Graph Databases, XGBoost, Excel Development, Microsoft Outlook, Data Interpretation

Senior Actuarial Consultant

2018 - 2021
Talcott Resolution
  • 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.
Technologies: R, Excel VBA, Simulations, Data Visualization, Predictive Modeling, Life Insurance, Financial Mathematics, Tail Risk, Statistics, SQL, Machine Learning, Data Science, RStudio, RStudio Shiny, Artificial Intelligence, Linear Optimization, H2O Deep Learning Platform, Clustering, Data Analysis, Data Science, Linear Regression, Principal Component Analysis (PCA), Genetic Algorithms, Predictive Analytics, Finance, Financial Data Analytics, Financial Data, Dashboard, Statistical Modeling, Data Science, Regression Modeling, Trend Analysis, Excel Macros, Excel Development, Microsoft Outlook, Visual Basic, Data Interpretation

Freddie Mac Credit Risk Analysis

https://gitlab.com/ddoyle_portfolio/credit-risk/-/blob/main/reporting/freddie_mac_report.md
This project analyzed the credit and prepayment performance of 46.77 million mortgages from Freddie Mac's data. It investigated the data to determine what variables drive performance and visualized the relationship between performance and explanatory variables. Neural networks were then built and trained to predict loan performance and identify impactful variables.
The repo linked to the project URL is private, so please email for access.

Personal Investment Management

https://github.com/DDoyle1066/InvestmentManagement
This process generates optimally diversified portfolios based on different risk thresholds. It proceeds by:
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 Metamodeling

https://github.com/DDoyle1066/VA_Metamodeling
This project walks through pricing a block of variable annuities and developing a faster metamodel for that approximation. It proceeds by:
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.
2015 - 2018

Bachelor's Degree in Actuarial Science

Robert Morris University - Moon Township, Pennsylvania, United States

OCTOBER 2022 - PRESENT

Reinforcement learning

Coursera

JANUARY 2020 - PRESENT

Associate of the Society of Actuaries

Society of Actuaries

Libraries/APIs

Pandas, TensorFlow, NumPy, Matplotlib, PySpark, XGBoost, PyTorch

Tools

Jupyter, Excel Development, Microsoft Outlook, Git, Seaborn, Tableau Development, Excel Development, Plotly

Languages

Julia, R, Python, Excel VBA, SQL, Visual Basic, C++

Frameworks

RStudio Shiny

Platforms

RStudio, H2O Deep Learning Platform, Databricks, Docker, Jupyter Notebook

Industry Expertise

Life Insurance

Storage

Neo4j, Database, Graph Databases

Other

Predictive Modeling, Probability Theory, Data Visualization, Simulations, Machine Learning, Data Science, Artificial Intelligence, Linear Optimization, Data Analysis, Data Science, Predictive Analytics, Finance, Financial Data Analytics, Financial Data, Dashboard, Statistical Modeling, Data Science, Regression Modeling, Trend Analysis, Excel Macros, Data Interpretation, Statistics, Risk Models, Tail Risk, Financial Modeling, Financial Mathematics, Deep Learning, Neural Network, 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, DataFrames

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