1. What is Data Science & Machine Learning?

Data Science and Machine Learning (ML) provide powerful tools for finding patterns, making predictions, and generating insights from complex and high-dimensional datasets. In academic and applied settings, these techniques are essential for uncovering structure in messy data, exploring heterogeneity, and automating decision-making processes.

Whether you’re working with clinical data, behavioral tracking, or large-scale surveys, I bring a research-grounded perspective to building and interpreting ML models that are robust, interpretable, and suited to your unique questions.

2. What I Offer

I help research teams and data-driven organizations design and implement machine learning workflows that are both statistically sound and practically actionable. My focus is on solving real problems — not black-box modeling.

My services include:

  • Designing ML pipelines for structured research data
  • Applying ML to detect subgroups, patterns, and latent structures
  • Feature engineering and model selection
  • Training and evaluating predictive models
  • Supporting explainability and reproducibility
  • Visualization and interpretation for academic or stakeholder audiences

Methods & techniques:

  • Supervised learning: regularized regression, decision trees, random forests, SVMs
  • Unsupervised learning: clustering, latent class/profiles, dimensionality reduction
  • Subgroup discovery in SEM and other statistical models
  • Ensemble models and cross-validation
  • ML integration with longitudinal or hierarchical data structures

I work primarily in Python and R, integrating statistical theory with state-of-the-art ML tools.

3. Consulting Packages

Each package can be tailored to your dataset, goals, and technical background. Whether you need a model to support a publication or a dashboard-ready analysis pipeline, we’ll define the right scope together.

Discovery & Design Session

A strategy session to:

  • Explore your dataset and research questions
  • Identify suitable modeling approaches
  • Clarify data needs and limitations

Best for clients who want to explore what’s possible with their data before committing to full development.

Custom Modeling Pipeline

A full ML workflow designed around your research or business problem:

  • Preprocessing, feature selection, modeling
  • Iterative refinement and validation
  • Clear interpretation and presentation of results
  • Optional: code handoff, documentation, or reporting materials

Ideal for research teams or data projects requiring robust, explainable models.

Ongoing ML Support

A collaborative partnership for evolving data projects:

  • Support across multiple analyses or datasets
  • Integration with other modeling or study planning efforts
  • Regular check-ins and deliverables based on your roadmap

Great for teams needing consistent ML expertise as part of a larger data strategy.

4. Who This Is For

  • Health, behavioral, and social science researchers using large or complex datasets
  • Applied teams aiming to move beyond traditional statistics into ML
  • Organizations needing interpretable, evidence-based modeling pipelines
  • Labs or PhDs looking for reproducible ML code and outputs for publication

5. Why Work With Me?

I combine academic training in Statistical Data Analysis with applied experience in machine learning for clinical and experimental research. From subgroup discovery in structural equation models to decision-tree-based health predictions, I bring methodological depth and practical focus to every project.

I’ve supported projects in academia, health science, and UX/data product development — always emphasizing clarity, reproducibility, and usefulness of results.

6. Ready to Get Started?

Let’s turn your data into insight. I’m happy to design a custom package based on your needs and timeline.