About
Theals is an independent
research infrastructure practice
Theals supports grants, large scale data work, statistical analysis, applied machine learning, and publication ready writing through a single reproducible workflow.
Led by a PhD scientific writer and an MD clinician scientist, supported by a doctoral trained team across writing, methods, and analytics.
Practice overview
What the practice delivers
Work is structured around defensible assumptions, reproducible pipelines, and reviewer aware communication. Deliverables are designed to survive peer review, reruns, and real world scrutiny.
Grant and study architecture
Specific aims, narrative coherence, feasibility, and analysis planning aligned to reviewer logic.
Reproducible data and methods
Cohort definitions, data dictionaries, QA, and pipelines designed for auditability and iteration.
Publication ready writing
Methods, results, and full manuscripts with claims discipline, figure logic, and revision strategy.
Assumptions are explicit
Every analysis and claim is traceable to documented definitions and stated assumptions.
Outputs are versioned
Tables, figures, and summaries are produced in a way that can be rerun and compared over time.
Evaluation matches decisions
Metrics and validation are chosen to match the real use case, not generic benchmarks.
Reviewer aware communication
Narrative structure is built around what reviewers test: novelty, validity, and clarity of claims.
Scope is defined
Engagements start with clear inputs, deliverables, timelines, and constraints to prevent drift.
Confidential by default
Work is handled with conservative assumptions about privacy, attribution, and data handling.
How we work
A small practice can move quickly without sacrificing rigor. The operating principle is simple: make assumptions explicit, make work reproducible, and make outputs legible to reviewers and stakeholders.
- 1
Rigor before velocity
Fast delivery is valuable only when the reasoning, definitions, and QA are defensible.
- 2
Reproducibility as a product
Pipelines, documentation, and artifacts are built so results can be rerun and extended later.
- 3
Clarity of claims
Writing and interpretation are disciplined to what the data supports, with limitations stated plainly.
What clients typically gain
Clear scope, fewer rewrites, and outputs that are easier to defend internally and externally.
Fewer revision cycles
Cleaner logic and clearer claims reduce back and forth with coauthors and reviewers.
Auditable analysis
Versioned outputs and QA make it easier to answer questions about methods and definitions.
Better decision making
Analysis and modeling framed around the decision, not just the dataset.
Online
Location
San Francisco
Remote first
Coming soon
By scoped engagement
Reach us
Contact
Phone number available on request