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A nerd forging nerds
Jameson A.

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Hourly Rate: $120
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About Jameson

Bio

I am a first-generation scholar with a Master’s degree in Statistics and a Ph.D. in Applied Economics, with extensive training in econometrics, quantitative modeling, causal inference, time-series analysis, and machine learning. I am also completing a Master’s degree in Computer Science with a machine learning specialization at Georgia Tech. My academic and professional background allows me to tutor students in statistics, data science, economics, research methods, programming, and...

I am a first-generation scholar with a Master’s degree in Statistics and a Ph.D. in Applied Economics, with extensive training in econometrics, quantitative modeling, causal inference, time-series analysis, and machine learning. I am also completing a Master’s degree in Computer Science with a machine learning specialization at Georgia Tech. My academic and professional background allows me to tutor students in statistics, data science, economics, research methods, programming, and quantitative finance. I work in quantitative finance and model risk at a large U.S. bank, where I evaluate complex credit-risk, stress-testing, and machine-learning models. I am fluent in English, Spanish, French, and Creole, and I am currently learning Italian.

I have mentored more than 30 high school students through Polygence in one-on-one online research settings. I help students refine research questions, locate and interpret credible sources, select appropriate methods, analyze data, write clearly about results, and prepare polished final projects. My approach is supportive and individualized: I meet students at their current level, break challenging concepts into practical steps, and emphasize hands-on learning through real-world examples and projects. I have also worked as a statistical consultant at the University of Georgia, advising students and faculty on experimental design, data collection, model selection, and statistical analysis.

I enjoy helping students build both technical confidence and intellectual curiosity. Whether a student is exploring a first research project, preparing for advanced coursework, or learning how data can answer real-world questions, I encourage them to ask thoughtful questions and develop independent problem-solving skills. My experience as a researcher, quantitative professional, and mentor helps me create a structured, encouraging environment for students with different interests, backgrounds, and goals.

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Education

Zamorano University
Business Engineering
Applied Economics
PhD
Statistics
Masters

Additional Languages

Spanish

Policies

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Approved Subjects

ACT Math

ACT Math

I have graduate training in statistics, applied economics, and computer science, and I work daily with quantitative models. My background covers algebra, functions, statistics, probability, and data interpretation. I teach students to read each question carefully, choose an efficient method, and verify the result under time pressure. Practice sessions focus on recurring patterns and clear time management.
Actuarial Science

Actuarial Science

My background combines an M.S. in Statistics, a Ph.D. in Applied Economics, and current work in quantitative model risk. At Huntington, I review credit, interest-rate, market-risk, economic-capital, and valuation models while evaluating methodology, data quality, assumptions, and performance. I have validated probability-of-default, loss-given-default, exposure-at-default, CCAR, and CECL frameworks across several lending portfolios. My lessons use worked examples and careful notation to connect probability, financial risk, and model interpretation.
Algebra 1

Algebra 1

My graduate work in statistics, applied economics, and computer science rests on the algebra taught in this course. I use equations, functions, graphs, and quantitative reasoning in research and model validation. I explain why each step works before asking a student to practice it independently. Short examples build fluency, then we apply the same ideas to word problems.
Algebra 2

Algebra 2

My work in research, banking, and statistical consulting uses the algebraic reasoning developed in Algebra 2. I am comfortable with functions, systems of equations, exponents, logarithms, polynomials, and graph interpretation. Lessons begin with the idea behind the problem, then we work through the algebra carefully. Students leave with methods they can reuse on homework and tests.
Calculus

Calculus

Calculus supports the optimization, modeling, and quantitative research I use in economics and data science. My graduate work has required careful reasoning with functions, rates of change, integrals, and applied modeling. I explain each procedure through graphs and small examples before moving to longer problems. We connect the calculation to the quantity it represents.
Computer Science

Computer Science

I am completing an M.S. in Computer Science at Georgia Tech with a specialization in machine learning. My work has involved Python, PySpark, C++, SQL, data pipelines, and production machine learning systems. At Wells Fargo, I built fraud models and converted Python code to PySpark for large-scale processing. I teach by tracing how code runs, testing small examples, and helping students build a clear path from the problem to a working program.
Data Analysis

Data Analysis

I hold a Ph.D. in Applied Economics and an M.S. in Statistics, along with a graduate certificate in data science. My professional work has covered data cleaning, exploratory analysis, visualization, feature engineering, and predictive modeling across academic, financial, and climate projects. As a statistical consultant at the University of Georgia, I advised researchers on study design, data collection, model selection, and the clear presentation of findings. During tutoring, I connect each technique to the student’s question and use small, reproducible examples to build confidence with the full analysis.
Differential Equations

Differential Equations

I use differential-equation thinking in applied economics, machine learning, and quantitative modeling. My graduate training in statistics and computer science gives me a strong foundation in functions, derivatives, systems, and model interpretation. I explain the connection between a solution method and the behavior of the system it describes. Worked examples show when each approach applies and how to check the answer.
Econometrics

Econometrics

I earned a Ph.D. in Applied Economics and have used econometric methods throughout my graduate research. My published and ongoing work applies panel data models, difference-in-differences, time series methods, cross-sectional analysis, and generalized linear models. I work in R, Stata, SAS, Python, and MATLAB, so students can learn the method in the software used for their course. Lessons begin with the research question and move through assumptions, model output, diagnostic checks, and plain-language interpretation.
Elementary Math

Elementary Math

I mentor students in quantitative subjects and make mathematics approachable through clear language and steady practice. My work as a statistical consultant and mentor has taught me how to identify the step that is causing confusion. I use visual models, familiar contexts, and short exercises so each idea has a concrete meaning. Lessons build accuracy alongside confidence.
Financial Accounting

Financial Accounting

I completed financial accounting coursework during my undergraduate engineering program and continue to work with financial concepts in applied economics and banking. My professional experience includes quantitative risk analysis across lending portfolios, model validation, and financial data. I can help students understand the accounting cycle, financial statements, journal entries, and the logic behind each balance. We work from transactions to statements so the relationships between accounts remain clear.
Geometry

Geometry

I bring graduate quantitative training and years of analytical work to the visual reasoning at the center of geometry. My research and modeling roles use coordinate systems, functions, measurement, and structured problem solving. I help students see the relationship between diagrams, definitions, and the equations that describe them. Each session includes clear sketches and targeted practice.
Microsoft Excel

Microsoft Excel

I use Excel in market research, data analysis, and reporting. My work has included cleaning large datasets, preparing quantitative summaries, and communicating results to clients and leadership. I also use Excel alongside statistical software and programming tools to build transparent analyses. In lessons, I begin with the student’s task and show how each formula, table, chart, or workflow answers a practical question.
Prealgebra

Prealgebra

I have extensive experience teaching and applying quantitative ideas through mentoring, research, and statistical consulting. Prealgebra gives students the language they need for later work in algebra, statistics, and science. I break down operations, fractions, decimals, ratios, and equations into short, connected steps. We practice until the student can explain the reason behind each calculation.
Precalculus

Precalculus

My advanced study in economics, statistics, and computer science relies on the functions and modeling introduced in precalculus. I work with polynomials, exponentials, logarithms, trigonometric ideas, and graph interpretation in quantitative settings. I help students connect symbolic work to the shape and behavior of a graph. Lessons build durable methods for calculus and other advanced courses.
Probability

Probability

I hold an M.S. in Statistics and use probability every day in model validation, risk analysis, and machine learning. My work includes probability of default, uncertainty, distributions, simulation, and statistical inference. I show students how a probability model captures a real question before we calculate. Examples make the rules intuitive and give the student a way to check each answer.
Python

Python

I use Python at an advanced level for data analysis, machine learning, model validation, and research. At Wells Fargo, I built fraud models and converted Python code to PySpark for large-scale processing. I also use Python in causal inference, time series analysis, and quantitative risk work. Lessons center on clear code, debugging habits, and small projects that match the student’s goals.
R

R

I use R at an advanced level for statistical modeling, causal inference, time series analysis, machine learning, visualization, and reproducible research. My doctoral research and consulting work have involved building regression models, preparing data, checking assumptions, and explaining results to varied audiences. I also work with R Markdown and R Shiny, so I can help with reports and interactive applications alongside core programming. During lessons, we write readable code around the student’s own problem and inspect each result as the analysis develops.
SAS

SAS

I have used SAS throughout graduate research and quantitative work in banking. At Truist, I developed and documented a parallel-processing framework on the SAS Grid for a risk-modeling team, while my research has used SAS for regression and empirical analysis. My experience covers data preparation, statistical procedures, model validation, performance review, and reproducible workflows. I teach SAS through small programs that make the DATA step, procedures, logs, and output easy to trace.
Spanish

Spanish

I am fluent in Spanish across speaking, reading, writing, and listening. I have worked with students from different countries as a quantitative research mentor and communicate clearly across academic settings. I can help students build grammar, vocabulary, conversation skills, reading comprehension, and written expression. Sessions adapt to the student’s level and place new language in practical contexts.
STATA

STATA

I use Stata at a high-intermediate level for applied economic research and causal inference. My Ph.D. work has involved panel data, cross-sectional models, time series methods, generalized linear models, and difference-in-differences analysis. I help students structure data, choose commands that match the research design, read model output, and check the assumptions behind an estimate. Lessons stay close to the student’s dataset so each command has a clear purpose and each result can be explained.
Statistics

Statistics

I hold an M.S. in Statistics and a Ph.D. in Applied Economics, with years of experience applying statistical methods in research and industry. As a statistical consultant at the University of Georgia, I advised faculty and students on experimental design, data collection, model selection, and quantitative interpretation. My work spans regression, probability, time series, panel data, generalized linear models, machine learning, and model validation. I teach from the meaning of the problem toward the calculation, using clear examples and software when it helps the student see how the method works.
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Hourly Rate: $120
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