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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
Data Annotation
Python, Mathematics, Statistics, Finance
Backyard Eats LLC.
Artificial Intelligence (AI), Optimization Algorithms, Layout, Excel VBA, Julia...
Zoetis - Main
SQL, Tableau, Data Analysis, Data Analytics, R, Bayesian Statistics, Python...
Experience
- Predictive Modeling - 5 years
- Statistics - 5 years
- Machine Learning - 5 years
- R - 4 years
- Simulations - 4 years
- Financial Mathematics - 3 years
- Python - 3 years
- Julia - 2 years
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
Data Scientist
2023 - PRESENT
Data Annotation
- Completed projects that involved crafting prompts to identify breaking points in LLMs. These prompts covered areas such as mathematics, programming, statistics, finance, and actuarial science.
- Critiqued large language models (LLMs) outputs by providing fine-grained rubrics to guide LLMs to the correct answer.
- Guided LLMs to the correct answer to teach them about math, finance, and statistics.
Technologies: Python, Mathematics, Statistics, Finance
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 (AI), Optimization Algorithms, Layout, Excel VBA, Julia, Plotly, Excel Macros, Microsoft Excel, Visual Basic for Applications (VBA), Excel Expert
Data Analyst
2022 - 2023
Zoetis - Main
- Transitioned and streamlined data processes 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 Bayesian machine learning model to detect contracts with outlying price concessions. Built a machine learning model to predict customer behavior given price changes and competitive actions to inform optimal pricing decisions.
Technologies: SQL, Tableau, Data Analysis, Data Analytics, R, Bayesian Statistics, Python, PySpark, Azure Databricks, Databricks, Risk, Pricing, Excel VBA, Excel 2013, Linear Regression, Git, Principal Component Analysis (PCA), Predictive Analytics, Financial Data, Dashboards, Statistical Modeling, Statistical Analysis, Databases, Regression Modeling, Jupyter, Trend Analysis, Excel Macros, Microsoft Excel, Microsoft Outlook, Visual Basic for Applications (VBA), Data Interpretation, Data Scientist, Excel Expert
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 (AI), Linear Optimization, Neural Networks, Pandas, Clustering, Data Analysis, Data Analytics, Linear Regression, Git, Principal Component Analysis (PCA), Optimization Algorithms, Predictive Analytics, APIs, Dashboards, Statistical Modeling, Statistical Analysis, Databases, Regression Modeling, Jupyter, Graph Databases, XGBoost, Microsoft Excel, Microsoft Outlook, Data Interpretation, Data Scientist
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 (AI), Linear Optimization, H2O Deep Learning Platform, Clustering, Data Analysis, Data Analytics, Linear Regression, Principal Component Analysis (PCA), Genetic Algorithms, Predictive Analytics, Finance, Financial Data Analytics, Financial Data, Dashboards, Statistical Modeling, Statistical Analysis, Regression Modeling, Trend Analysis, Excel Macros, Microsoft Excel, Microsoft Outlook, Visual Basic for Applications (VBA), Data Interpretation, Data Scientist, Excel Expert
Experience
Freddie Mac Credit Risk Analysis
https://gitlab.com/ddoyle_portfolio/credit-risk/-/blob/main/reporting/freddie_mac_report.mdThis 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.
The repo linked to the project URL is private, so please email for access.
Personal Investment Management
https://github.com/DDoyle1066/InvestmentManagementThis 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.
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_MetamodelingThis 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.
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.
Education
2015 - 2018
Bachelor's Degree in Actuarial Science
Robert Morris University - Moon Township, Pennsylvania, United States
Certifications
OCTOBER 2022 - PRESENT
Reinforcement learning
Coursera
JANUARY 2020 - PRESENT
Associate of the Society of Actuaries
Society of Actuaries
Skills
Libraries/APIs
Pandas, TensorFlow, NumPy, Matplotlib, PySpark, XGBoost, PyTorch
Tools
Jupyter, Microsoft Excel, Microsoft Outlook, Git, Seaborn, Tableau, Excel 2013, Plotly
Languages
Julia, R, Python, Excel VBA, SQL, Visual Basic for Applications (VBA), C++
Frameworks
RStudio Shiny
Platforms
RStudio, H2O Deep Learning Platform, Databricks, Docker, Jupyter Notebook
Industry Expertise
Life Insurance
Storage
Neo4j, Databases, Graph Databases
Other
Predictive Modeling, Probability Theory, Data Visualization, Simulations, Machine Learning, Data Science, Artificial Intelligence (AI), Linear Optimization, Data Analysis, Data Analytics, Predictive Analytics, Finance, Financial Data Analytics, Financial Data, Dashboards, Statistical Modeling, Statistical Analysis, Regression Modeling, Trend Analysis, Excel Macros, Data Interpretation, Data Scientist, Excel Expert, Statistics, Risk Models, Tail Risk, Financial Modeling, Financial Mathematics, Deep 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, DataFrames, Mathematics
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