Scan
A focused diagnostic of the workflow, evidence base, data quality, stakeholder pressure, and decision bottleneck.
- Clarify the real problem
- Map current data and systems
- Identify high-leverage next steps
I am William T. Barker, PhD, Founder of SignalForge Advisors. I help biotech, pharma, and life-sciences teams translate noisy data, fragile evidence, omics outputs, and AI/ML workflows into clear decisions that can survive scientific, operational, and stakeholder scrutiny.
SignalForge Advisors exists for the middle ground where science is real, data is imperfect, decisions are urgent, and generic AI or dashboard work is not enough. The goal is not more noise. The goal is signal that helps teams decide what to do next.
A focused diagnostic of the workflow, evidence base, data quality, stakeholder pressure, and decision bottleneck.
A contained build or analysis that tests whether a data, AI, or scientific workflow can produce decision-useful output.
Ongoing advisory and execution support for teams that need a scientific operator who can bridge domain depth, data systems, and leadership communication.
My background spans organic chemistry, medicinal chemistry, high-throughput screening, microbiology, influenza virology, reverse genetics, vaccine R&D, omics, bioinformatics, and applied machine learning.
I founded SignalForge Advisors to help technical teams use AI and data systems with discipline. That means asking better questions, cleaning the data layer, understanding the scientific context, and building workflows that create useful decisions rather than impressive-looking artifacts.
Advising life-sciences teams on scientific strategy, AI/data workflow design, omics interpretation, and decision-grade communication.
Supported seasonal influenza vaccine R&D through hands-on virology, reverse genetics, RNA-seq, data analysis, and cross-functional scientific problem solving.
Contributed to drug discovery and screening programs around antibiotic resistance, biofilms, and small-molecule adjuvant strategies.
Trained in synthesis, drug screening, ADME-aware thinking, structure-activity relationships, and the design of small-molecule research programs.
Convert messy R&D questions, contradictory readouts, and incomplete evidence into a clear model of what is known, unknown, and decision-relevant.
Design, audit, and explain RNA-seq and related analytical workflows so outputs become biologically meaningful and review-ready.
Integrate LLMs and ML tools into real work with guardrails, measurement, quality checks, and honest boundaries around what the models can and cannot do.
Translate complex science into executive briefs, stakeholder narratives, decision memos, project plans, and operating documents.
Improve fragile scripts, manual analyses, and ad hoc workflows into cleaner, repeatable systems that a team can trust.
Bring a cross-domain perspective across small molecules, virology, vaccine manufacturing, data science, and AI-enabled research operations.
I am most useful when the work is ambiguous, technical, cross-functional, and important enough that the answer needs to be both scientifically honest and operationally useful.