Jacob Pieniazek
Jacob Pieniazek
Welcome!
I am a Lead Data Scientist at 84.51° in Cincinnati, OH, a persistent learner, and, most importantly, a devoted friend, son, brother, and boyfriend.
My deep passion for data science was fostered and stimulated through my Master's studies in Economics at Miami University in Oxford, OH and, prior to graduate school, my studies in Mathematics and Economics at the University of Dayton.
My fields of expertise concentrate heavily in applied econometrics, mathematical optimization, statistical/machine learning, and bridging the conceptual gap between causal inference and predictive modeling.
In the professional sphere, I am dedicated to developing scalable & production-grade econometric, causal inference, and machine learning solutions for measurement & personalized targeting within the retail marketing industry, with expertise in both experimental and quasi-experimental settings.
Outside of work, I like developing python packages 🐍, exploring self-hosting & home networking + infrastructure design 🖥️, sharing knowledge through blog bosts ✍️, spending time outdoors hiking and camping ⛺, playing the guitar 🎸, and immersing myself in music—especially jam bands & improvisational jazz 🎼
Check out my blog posts and personal projects
What am I listening to right now on Spotify?
Want more?
Welcome!
I am a Lead Data Scientist at 84.51° in Cincinnati, OH, a persistent learner, and, most importantly, a devoted friend, son, brother, and boyfriend.
My deep passion for data science was fostered and stimulated through my Master's studies in Economics at Miami University in Oxford, OH and, prior to graduate school, my studies in Mathematics and Economics at the University of Dayton.
My fields of expertise concentrate heavily in applied econometrics, mathematical optimization, statistical/machine learning, and bridging the conceptual gap between causal inference and predictive modeling.
In the professional sphere, I am dedicated to developing scalable & production-grade econometric, causal inference, and machine learning solutions for measurement & personalized targeting within the retail marketing industry, with expertise in both experimental and quasi-experimental settings.
Outside of work, I like developing python packages 🐍, exploring self-hosting & home networking + infrastructure design 🖥️, sharing knowledge through blog bosts ✍️, spending time outdoors hiking and camping ⛺, playing the guitar 🎸, and immersing myself in music—especially jam bands & improvisational jazz 🎼
Check out my blog posts and personal projects
Jacob Pieniazek
Jacob Pieniazek
Welcome!
I am a Lead Data Scientist at 84.51° in Cincinnati, OH, a persistent learner, and, most importantly, a devoted friend, son, brother, and boyfriend.
My deep passion for data science was fostered and stimulated through my Master's studies in Economics at Miami University in Oxford, OH and, prior to graduate school, my studies in Mathematics and Economics at the University of Dayton.
My fields of expertise concentrate heavily in applied econometrics, mathematical optimization, statistical/machine learning, and bridging the conceptual gap between causal inference and predictive modeling.
In the professional sphere, I am dedicated to developing scalable & production-grade econometric, causal inference, and machine learning solutions for measurement & personalized targeting within the retail marketing industry, with expertise in both experimental and quasi-experimental settings.
Outside of work, I like developing python packages 🐍, exploring self-hosting & home networking + infrastructure design 🖥️, sharing knowledge through blog bosts ✍️, spending time outdoors hiking and camping ⛺, playing the guitar 🎸, and immersing myself in music—especially jam bands & improvisational jazz 🎼
Check out my blog posts and personal projects
What am I listening to right now on Spotify?
Want more?
Welcome!
I am a Lead Data Scientist at 84.51° in Cincinnati, OH, a persistent learner, and, most importantly, a devoted friend, son, brother, and boyfriend.
My deep passion for data science was fostered and stimulated through my Master's studies in Economics at Miami University in Oxford, OH and, prior to graduate school, my studies in Mathematics and Economics at the University of Dayton.
My fields of expertise concentrate heavily in applied econometrics, mathematical optimization, statistical/machine learning, and bridging the conceptual gap between causal inference and predictive modeling.
In the professional sphere, I am dedicated to developing scalable & production-grade econometric, causal inference, and machine learning solutions for measurement & personalized targeting within the retail marketing industry, with expertise in both experimental and quasi-experimental settings.
Outside of work, I like developing python packages 🐍, exploring self-hosting & home networking + infrastructure design 🖥️, sharing knowledge through blog bosts ✍️, spending time outdoors hiking and camping ⛺, playing the guitar 🎸, and immersing myself in music—especially jam bands & improvisational jazz 🎼
Check out my blog posts and personal projects
Focus Areas
A quick focus map, plus my core strengths & what I tend to build day-to-day.
Econometrics/Causal Inference
Statistics/Statistical Learning
Mathematical Optimization
Time Series Analysis
Artificial Intelligence
DevOps + MLOps
Data Pipelining/Engineering
Package Development
Python Data Science Ecosystem
Languages I work in
Python
SQL
Rust
R
Bash
Stata
Core strengths
Causal Inference, Causal ML, & EconometricsStatistical Learning, ML, & AIMLOps / DevOps & Delivery / Package DevelopmentData Platforms & Analytics EngineeringCausal Inference, Causal ML, & Econometrics
I mostly work on program evaluation and modeling heterogeneous treatment effects - getting to who benefits, and by how much, not just a single average.
- CATE / uplift modeling, treatment targeting, and 'Causal ML'
- Traditional econometrics and quasi-experimental design & methods
- Identification + robustness checks (sensitivity, diagnostics)
- Turning research-y work into reusable pipelines
Representative tools:
Statistical Learning, ML, & AI
I build predictive models and practical AI workflows, with a strong emphasis on evaluation, maintainability, and making outputs usable for real decisions.
- Model selection, calibration, and monitoring-ready metrics
- Interpretable ML and stakeholder-friendly explanations
- Agentic workflows + LLM tooling where it genuinely helps
Representative tools:
MLOps / DevOps & Delivery / Package Development
I like shipping things that other people can actually run: packages, services, and reproducible environments that don't crumble a week later.
- Packaging + dependency management (reproducible builds)
- Containerization and CI-friendly workflows
- Docs, tooling, and small developer experience improvements
Representative tools:
Data Platforms & Analytics Engineering
I build data foundations that are reliable, fast, and pleasant to use, so analysis and models don't start from chaos every time.
- Lakehouse patterns + distributed compute
- Query performance, reproducible datasets, and data quality checks
- Azure + Databricks for production workflows
Representative tools:
Built with Reflex