Ten years turning noisy biology into clean signal, from single-cell isolation and multicolor FACS to NGS across Illumina, Nanopore & PacBio and an 11,161 bp synthetic viral genome built from scratch.
The story goes that at three years old, Taylor was already interrogating the workings of the universe, and was unimpressed when the answers didn't come. Science fairs were the highlight of every school year (volcanoes, tornado chambers, the works), and A Brief History of Time was leisure reading. Growing up on the Gulf Coast of Florida, that curiosity ran on two tracks: fishing sharpened a fascination with the biology of fish, while playing baseball turned into an obsession with the physics of the game. A fifth-grade teacher predicted a career in shark biology. The species was off; the trajectory wasn't.
That curiosity became a decade of hands-on molecular biology spanning academia, an early-stage biotech startup, and big pharma. Taylor studied Biomedical Sciences at the University of South Florida with a minor in Biomedical Physics, then earned an M.S. at Auburn University's College of Veterinary Medicine, where a pivot from reproductive medicine into cancer biology and precision medicine set the course for everything that followed.
"The through-line of my work is signal extraction: pulling meaningful sequence out of the messiest biological material, then building the method so anyone can do it again."
At Auburn, Taylor developed the first published protocol for isolating melanocytes from canine skin and oral mucosa, a rare-cell problem where the target makes up just 3-5% of skin. Recruited straight out of the thesis defense by the founders of Humane Genomics, Taylor became one of the company's first three employees and built an entire synthetic-virology lab from the bench up: qPCR pipelines, Nanopore sequencing workflows, and viral vectors engineered for precision cancer therapy. Later chapters at Merck and Azenta Life Sciences deepened that method-development rigor across high-throughput, multi-platform sequencing.
GLP/GCP-trained and fluent in translating complex biological problems into actionable research strategy, Taylor now works at the intersection of gene editing, viral vectors, and next-generation sequencing, and is eager to apply that method-development rigor within a cGMP analytical-development environment supporting first-in-human and IND products.
A path that runs the full arc of the field. Building bench science from day one at a three-person startup, then carrying that method-development discipline into large-pharma and CRO environments.
Each of these started as a hard "how do we even measure this?" question and ended as a repeatable method. The kind of work that becomes a platform.
Co-developed a synthetic-biology platform to rapidly engineer Vesicular Stomatitis Virus from the ground up. A full-length genome was assembled from modularized DNA fragments, rescued, and titered, with phenotypic analysis showing no significant difference between natural and synthetic virus. The platform's flexibility was demonstrated by swapping a foreign glycoprotein and rearranging gene order (VSV-P ⇄ VSV-M) to prove design freedom.
At the height of the pandemic, the team built a SARS-CoV-2 vaccine candidate on the same VSV backbone behind Merck's approved Ebola vaccine, swapping the native glycoprotein for the SARS-CoV-2 spike protein. Designs went from concept to functional virus particles in under a month. Live-cell Incucyte imaging confirmed the vaccine displayed spike on its surface and bound the ACE2 receptor like the natural virus. Taylor scaled production 100-1000x for animal work, and a rat study at Auburn's Scott-Ritchey Research Center showed the candidate was well tolerated with no adverse reactions (CBC & clinical chemistry confirmed). The team wrote and submitted a pre-IND to the FDA before winding the program down once authorized vaccines reached the public.
Program write-ups: Proposal ↗ · Scientific basis ↗ · Lab update ↗ · Final results ↗
Built a single end-to-end workflow that takes a candidate virus from harvested supernatant to called sequence in one day. The route runs supernatant harvest → viral RNA isolation → cDNA synthesis → PCR amplification → library prep on the Oxford Nanopore Rapid Barcoding Kit → sequencing → assembly and analysis. Barcoding let the method multiplex up to 24 viral genomes in a single run, turning what had been a serial, multi-day sequencing task into a parallel screen. The result is a same-day readout that lets a bench scientist confirm construct identity across a whole panel of viral candidates within 24 hours, fast enough to steer the next round of engineering.
Built protocols to isolate the normal-cell counterparts of three skin tumors (melanocytes, keratinocytes & mast-cell progenitors) as transcriptomic comparators for precision oncology. Delivered the first published method for canine melanocyte isolation, now independently reproduced by other labs.
When low RNA yield & purity from viral passaging broke one-step RT-qPCR, Taylor engineered a two-step method that cleanly quantified viral genomes across 10³ to 10¹² genome copies, the titer data that dosed in vivo mouse studies, and the seed of the lab's genome-sequencing workflow.
Engineered oncolytic VSV to selectively kill GPC3-positive liver-cancer cells, pairing a retargeted glycoprotein with aptazyme replication switches. Showed infection & lysis at low MOI with no effect in GPC3-negative cells, and 10⁷ PFU doses well tolerated in vivo.
Built an indirect viral-sequencing route (RNA → cDNA → dsDNA → Nanopore) to sequence engineered virions base-by-base for QC, then compressed the plasmid workflow all the way down to miniprep-to-analysis in two hours.
A service concept for scaling native RNA-seq from single-sample pilots to 96-plex cohort studies, sequencing the true RNA strand with zero cDNA or PCR bias, on Oxford Nanopore's SQK-RNA004 chemistry.
Native RNA sequencing has always traded cost for authenticity: one flow cell, one sample, real modifications preserved. 96-barcode multiplexing changes that math: pool an entire well-plate onto a single PromethION run and the per-sample cost falls from roughly $900 to about $10, without giving up direct-strand sensing.
From nucleic-acid extraction to base-called, aligned, variant-called data, with the assay-development and QC rigor to make each step reproducible.
A self-built web application for navigating biotech careers, developed hands-on with modern AI tooling, the same skillset behind the Google AI Professional Certificate above. Turning credentials into shipped software.
A decade of output spanning a peer-reviewed journal article, conference posters, a Master's thesis, and contributions to patented synthetic-virology technology.
Precision oncology needs the normal counterpart of a tumor to compare against. These are the wet-lab methods I developed at Auburn to pull three hard-to-isolate normal cell types out of canine skin and blood. Each one a repeatable, single-cell-sequencing-grade workflow. Tap any step for detail.
Melanocytes are only 3-5% of skin cells. First published method for canine skin & oral mucosa.
Keratinocytes are ~85% of skin, so yield is high. Comparator for basal & squamous cell carcinoma.
Skin mast cells are <1% of cells and hard to purify, so the target moved to circulating progenitors.
Full parameters, controls and gating strategy are in the M.S. thesis and the progenitor / melanocyte posters.
Since 2024, Taylor has owned and operated a weekly trivia business spanning multiple Jersey City venues. The same rigor for accuracy that lives at the bench, pointed at pop culture and bar crowds.
The role is equal parts host, statistician, and small-business owner: curating and fact-checking quizzes, running the room for 200+ guests, managing staff, and negotiating client contracts, all on a tight weekly schedule with real-time scorekeeping and spreadsheet wrangling.
To keep regulars coming back, Taylor built a custom loyalty-leaderboard app that ingests every night's scores and turns them into a living season standing. Each team earns a Loyalty Score and a tier (Casual → Intermediate → Pro), with trend arrows tracking who's heating up week to week.
The twist is a set of alternative standings designed so the same few powerhouse teams don't win everything, giving newer and casual teams their own races to chase: a Handicap Cup that rewards beating your own past average, a Giant Slayer Cup for upsetting dominant teams, and Median Battles crowning the most consistently middle-of-the-pack squad.
A job search engine for life-sciences scientists. Drop in a CV and it reads your skills, then ranks live postings pulled straight from the job boards of 79 biotech companies. Built because every existing job site matches on keywords and job titles, which is a terrible proxy for whether a bench scientist can actually do the work.
Most job boards search the text of a posting. This parses the CV against an ontology of 67 canonical biotech skills — AAV, oncolytic vectors, RT-qPCR, Nanopore, FACS, UF/DF, cGMP — and builds a weighted profile of what you can actually do at the bench.
Every posting is then scored against that profile. It surfaces what a keyword search misses, and shows the gaps too: skills a role wants that your CV doesn't evidence.
There is no backend. The PDF is parsed by JavaScript in your own browser, and the postings come from the public ATS APIs that companies already serve. Nothing is stored, and nothing is sent anywhere.
PDF.js extracts the text in-browser, rebuilding line structure so job titles stay distinguishable from body prose.
Skills are claimed by trigger terms and damped on a log scale, so a word that's merely common in prose can't outrank a specialised technique.
Greenhouse, Lever, Ashby and SmartRecruiters are queried directly. Every link goes to the company's own board, so nothing is stale or reposted.
Roles are matched against your core skills and career level, then filtered by commute radius: 25, 50, 100 miles, or remote.
Open to senior research-scientist and analytical-development roles in gene & cell therapy, NGS, and synthetic biology.