Bioinformatics · Data Science · Cross-Practice
We recruit the bioinformaticians, computational biologists, pipeline engineers, and data scientists who turn sequencing and assay data into products — for genomics, diagnostics, and biotech organizations. We know the difference between a researcher who writes analysis scripts and an engineer who can build a validated clinical pipeline.
25 years recruiting inside diagnostics, genomics, and life science organizations — the teams that build and run these pipelines every day.
Each of these environments runs computational work differently. The data types, the regulatory burden, and the production bar all change.
Clinical bioinformaticians, pipeline developers, and variant analysts for genomics reference labs, specialty testing organizations, and hospital molecular labs — roles that sit close to production, where validated pipelines and reportable results matter.
CLIA Lab Recruiting →Bioinformatics scientists, algorithm developers, and software engineers building the data layer around NGS and molecular platforms — variant calling, secondary and tertiary analysis, and the reporting pipelines customers depend on.
IVD Recruiting →Computational biologists, biostatisticians, and data scientists supporting discovery, translational research, and clinical programs across biotech, pharma, and CRO environments.
CDMO/CMO Recruiting →Machine learning scientists and image-analysis engineers for computational pathology and AI diagnostics — the teams building and validating models that read images and molecular signals.
Life Science Tools Recruiting →Scientific informatics and data professionals for discovery, translational, and biobanking environments — multi-omics data management, pipeline integration, and the workflow engineering that follows a platform rollout.
Diagnostics Recruiting →Permanent and direct-hire professionals — from hands-on pipeline development through computational leadership.
Bioinformatics Scientist · Computational Biologist · Genomics Scientist · Variant Analyst · Clinical Genomics Scientist · NGS Data Analyst · Bioinformatics Analyst
Pipeline Developer · NGS Pipeline Engineer · Workflow Engineer (Nextflow / Snakemake / WDL) · Secondary Analysis Developer · Bioinformatics Software Engineer · Automation Engineer
Data Scientist · Machine Learning Engineer · AI/ML Scientist · Deep Learning Researcher · Computational Pathology Scientist · Image Analysis Engineer
Biostatistician · Statistical Geneticist · Computational Statistician · Bioinformatics Statistician · Population Genomics Scientist · Clinical Biostatistician
Data Engineer · Cloud / DevOps Engineer (AWS / GCP / Azure) · Data Architect · Bioinformatics Infrastructure Engineer · MLOps Engineer · Database Engineer
Director / VP of Bioinformatics · Head of Computational Biology · Head of Data Science · Chief Data Officer · Director of Genomics · Scientific Software Engineer
Bioinformatics and data science sit between the science, software engineering, and — in diagnostics — the clinic. Very few candidates are credible in all three, and generalist technology recruiters cannot tell the difference.
Research code is not production code. A bioinformatician who publishes analyses is not automatically one who can build a reproducible, versioned, validated pipeline a clinical lab depends on. We screen for engineering discipline — version control, testing, reproducibility — not just a publication record.
In regulated environments, analysis is validation work. For a diagnostic product, bioinformatics means validated, auditable pipelines. CLIA/CAP fluency and data-integrity discipline are screening criteria, not nice-to-haves — usually the single biggest predictor of whether a hire works out.
Machine learning and image-analysis scientists are a separate talent pool again. Computational pathology and AI diagnostics demand someone who understands both the model and the underlying biology or clinical signal. Pure ML engineers rarely translate; the candidates who bridge both are a small, well-networked group.
Data engineering at genomics scale is its own discipline. Pipeline orchestration, cloud cost-awareness, and reproducible infrastructure require engineers who are not the same people writing the analysis. The strongest are usually found inside genomics companies, not on general software job boards — that is the network we have spent 25 years building.
Bioinformatics leadership is scarce and consequential. A Head of Bioinformatics has to bridge science, engineering, and — increasingly — regulatory and product. We help clients define the right role before beginning the search.
Generalist tech recruiters treat these as ordinary software requisitions — submitting web developers for pipeline roles and ML generalists with no biology for genomics work. The submittals look reasonable on paper and fail in the technical screen. Knowing why is most of the job.
What the first two weeks look like when you bring us a bioinformatics or data science requirement.
ENGAGEMENT MODEL · PERMANENT & DIRECT-HIRE SEARCH
Scoping — We start with the science and the stack, not the job title. Which data types, which pipelines and tools, which cloud environment, which regulatory context (research, CLIA/CAP, GxP), and whether the work is a defined project or an ongoing seat. That conversation usually reshapes the requisition.
Search — We source from operating labs, diagnostics and genomics companies, and product engineering teams rather than open job-board applicants. Candidates are screened against the specific data types, tools, pipeline stack, and regulatory context — as well as their ability to own the work long term.
Submittal — You receive a short, qualified slate with a written summary of each candidate's technical depth, domain experience, availability, and compensation expectations. If we do not believe we can fill the role, we say so early rather than sending volume.
Common questions from employers hiring in bioinformatics and data science.
Yes — and the distinction is central to how we screen. Research bioinformaticians explore and analyze; production and clinical bioinformaticians build reproducible, validated pipelines a lab runs on. We identify which one your role needs and recruit accordingly.
Yes. Clinical pipeline development — validation, version control, and auditability under CLIA/CAP — is a distinct, hard-to-fill profile. We've placed candidates who've stood up validated production pipelines, not just research workflows.
Yes. We recruit deep-learning and image-analysis scientists for computational pathology and AI diagnostics, including candidates who bridge the ML and the clinical or biological domain.
All three. Much bioinformatics and data science work runs remotely, while some clinical validation and cross-functional roles want onsite time. Telling us the split up front widens or narrows the pool, so we ask early.
Laboratory informatics roles sit alongside these practices. Many searches touch more than one.
Clinical Labs
Lab operations, automation, LIS, revenue cycle, and executive search for reference and hospital laboratories.
Explore →Commercial, technical, and operational talent across tools, reagents, biotech, CRO, and CDMO organizations.
Explore →Diagnostics
Specialized recruiting across CLIA labs, IVD manufacturers, molecular diagnostics, pathology, and point-of-care.
Explore →Describe the platform, the project, and the timeline. We will tell you quickly whether we can help — and if we cannot, we will say so.
