Maintain consistent file structures, naming conventions, templates, and intake/QC checklists to reduce rework and improve traceability.
Prepare observational/registry/natural history/survey datasets by identifying data issues, resolving formatting problems per specs, and escalating anomalies to senior team members.
Create auditable records of data checks, changes, decisions, and versioning aligned to internal standards/SOPs.
Coordinate exports and data intake (as applicable), organize metadata (codebooks/labels), and ensure data provenance is documented.
Prepare reporting-ready tables, figures, and exhibits aligned to approved shells/specs, including formatting, labeling, and cross-references.
Review R scripts/outputs for reproducibility and spec alignment; QC draft deliverables for completeness/consistency before senior scientific review.
Support visualizations and drafting of methods/process and data handling text under senior guidance (no independent interpretation).

Bachelor’s degree in a quantitative or health-related discipline (Master’s a plus).
Experience supporting COA or outcomes research deliverables in consulting/CRO/academic/industry settings, working alongside psychometricians/statisticians.
Hands-on experience cleaning, standardizing, and QC-checking non-interventional datasets, with strong documentation and an audit-trail mindset.
R proficiency (delivery-focused): Able to run/maintain existing scripts, generate standardized outputs, and perform basic code/output QC (reproducibility, input/output traceability).
Demonstrated ability to produce accurate, client-ready tables/figures with strong formatting, labeling, referencing, and version control.
Detail-oriented, deadline-reliable, clear communicator who escalates issues appropriately and collaborates well across teams.
Comfortable handling sensitive information in line with data protection expectations.

Quantitative Research Analyst

Key skills