Data Science, Analytics & Research Engineering

Services

Focused data analysis and engineering for research environments.

Short-term, fixed-scope consulting sprints designed for labs and research-driven teams that need clear deliverables across research engineering, data science, and exploratory analysis without long-term hiring.

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Technical focus

Practical methods and data types I work with across research engineering and applied ML.

Time series Deep learning Machine learning Image processing Optimization Microscopy data Neural data Statistics Probabilistic models Signal processing Computational imaging Medical imaging Graph Reproducibility QC + diagnostics Experimental pipelines

Service areas

Each engagement is scoped to a specific outcome. If your need is broader, I will help narrow it to a workable sprint.

Exploratory analysis

Rapid, structured analysis to understand datasets and inform next steps. Analysis tailored to your experiments and data types.

Data science / Machine Learning (ML)

Applied machine learning, feature engineering, and evaluation for questions that benefit from statistical or computational approaches.

Pipeline design

Build or redesign data pipelines for imaging, experimental, and survey data with attention to traceability and repeatability.

Reproducible analysis

Package workflows and environments so results can be rerun by your team or collaborators.

Performance optimization

Identify bottlenecks and apply targeted improvements without rewriting entire systems.

Custom tooling

Create internal tools for data management, lab workflows, or analytics operations.

Documentation & handoff

Leave your team with clear documentation and a stable foundation for further development.

Don't see what you're looking for? Send a short project brief.

Selected projects

Outcome-focused engagements spanning data engineering, experimental systems, and applied ML for research teams.

Deep learning-based image processing

Problem

We have terabytes of imaging/video data and analysis is slow, fragile, and not reproducible.

Approach

  • End-to-end computer vision tools including deep learning models for feature extraction, classification, segmentation, tracking, etc.

Deliverables

  • Reproducible piepline deployable to HPC and cloud.

Impact

  • Reduced time-to-analysis (weeks to hours).
  • Production-grade data engineering beyond modeling.

High-dimensional data and time series analysis

Problem

We need preliminary insights on new datasets.

Approach

  • Data processing and analysis methods tailored to your datasets

Deliverables

  • A report outlining preliminary findings and suggested next steps.
  • Jupyter notebooks to reproduce computaiton and analysis.

Impact

  • Jump start your analysis with insights from an experienced researcher

Performance optimization

Problem

Our code is too slow and hindering our work.

Approach

  • Profiling and code optimization
  • Algorithm optimization, allocation optimization, parallelization (including multi-threading, CUDA, and HPC)

Deliverables

  • Optimized code.
  • Benchmark statistics comparing before and after

Impact

  • Clients often get multiple orders of magnitude improvements for key components in their projects

Custom interactive web app for data visualization

Problem

We need an interactive web app to visualize and share our datasets and findings.

Approach

  • Tailpred interactive plots for time series, graphs, images, etc.
  • Cloud (GCP, AWS, etc.) for reproducible, reliable, and scalable deployment.

Deliverables

  • Web app code and documentation.
  • Deployment to cloud or server for either public or private access.

Impact

  • Standardized web tool to visualize complex datasets for everyone in the team.
  • Share research datasets and findings to increase impact.

Quality Control + Failure Mode Diagnostics for Scientific ML Pipelines

Problem

We get inconsistent results across days, rigs, operators, cohorts.

Approach

  • Root-cause workflow from instrument to preprocessing to model.
  • Drift detection and QC evaluation tool.

Deliverables

  • Failure mode diagnostics and suggested improvements.
  • QC code and models to check new datasets.

Impact

  • Troubleshoot general errors, low model performance, or repeatability issues.

Good fit

  • Well-defined technical objective and clear stakeholders.
  • Need for exploratory analysis to shape research decisions.
  • Need for reproducible, maintainable code.
  • Team can provide data access and quick feedback.
  • Preference for concise documentation and clean handoff.

Not a fit

  • Open-ended research collaborations or grant partnerships.
  • Long-term staff augmentation or hiring.
  • Projects that require unplanned turnaround.
  • Work that cannot be scoped to a sprint.