Data Science

Despite the plethora of data available to us today, data is often underutilized due to time constraints and formulaic approaches that preclude original and thoughtful data modeling. Most organizations sit on more data than they know what to do with, and a shortage of people who can move fluidly between the statistics, the substantive business question, and the engineering needed to get from raw tables to actions and decisions. That's the gap my work can close: original, carefully-reasoned data science, scoped around your specific needs and goals, rather than a one-size-fits-all pipeline.

Every engagement starts with a conversation to understand what you're trying to learn or predict, what data you have on hand, and what a positive outcome looks like for your team. My work runs the gamut from data discovery, data evaluation, data transformation, to data analytics and visualizations grounded in statistical learning algorithms spanning both predictive and exploratory models. I routinely collaborate with subject matter experts with deep knowledge in substantive domains; and I love coaching or collaborating with clients’ in-house data scientists to bring ideas to fruition. I particularly enjoy new product development grounded in robust methodology.

What are your data needs? My past engagements include:

Deep mining of existing database

Most valuable insight is already sitting in data you've collected but haven't fully exploited. This means structured exploration of existing tables to reveal signals, patterns, anomalies, and relationships worth acting on — before reaching for a new data source or a bigger model.

Feature engineering

Before any model is built, careful work goes into deciding which variables matter, how they should be transformed, and how missing data should be handled. This includes multiple imputation for missing values, item analysis and variable transformation, optimal scaling, and data ipsatization — done either independently or hand-in-hand with your team, since this stage quietly determines how good any downstream model can be.

Predictive models

Predictive modeling only earns its keep if it holds up beyond the sample on which it was built. My toolkit spans linear and logistic regressions, multi-level/mixed models, support vector machines, decision trees, random forests, gradient-boosted trees (XGBoost/LightGBM), and neural networks — matched to your data structure and validated with an eye toward replicability, not just in-sample fit. Causal inference go beyond correlation to identify which factors actually move the key outcome of interest.

Integrating and analyzing multiple data streams

Answers rarely live in a single table. This covers combining transactional, behavioral, survey, and third-party data sources into a coherent analytic file — reconciling different granularities, time windows, and identifiers along the way — so models reflect the full picture rather than whichever dataset was easiest to access.

Data visualization

Good visualization is a thinking tool, an aid to stimulate ideas and solutions beyond mere aesthetics. Fresh, well-considered visual approaches help surface patterns that summary statistics hide and make findings legible to non-technical stakeholders.

Designing and analyzing A/B tests

Rigorous experimentation is the most reliable way to establish that a change actually causes an outcome, rather than merely coinciding with it — from monadic A/B designs to more complex factorial experiments, requiring corresponding repeated measures ANOVA, conditional logit, and other appropriate choice modeling statistical tests.

Data discovery to support or refute hypotheses

Sometimes the highest-value engagement isn't building a model at all — it's rigorously interrogating whether a hypothesis your team already holds is actually supported by the data, before resources are committed based on an untested assumption.

Model deployment & monitoring

After initial model is set up, I can also support moving a validated model from notebook to production, and setting up drift and performance monitoring, and iterative updates so the model stays relevant and responsive over time.

Shiny Apps

Interactive web apps for simulations and calculators are also built where useful — providing your team with a platform to efficiently explore "what-if" scenarios without the need to wait for new analysis output each time.