September 12, 2025
ScitoVation is the premier startup company for scientists who want to explore cutting-edge research and make a significant impact in the toxicology industry. You will be part of a highly-sought-after team of thought leaders and collaborators who are dedicated to finding innovative solutions to assess the safety of chemicals and products. Our team is driven by using the best science in chemical safety to improve risk assessment and decision making for clients. We are also at the forefront of developing next generation new approach methodologies (NAMs) and spearheading the efforts required to get key-stakeholder buy in ranging from regulatory bodies to pharma organizations. It’s an exciting time to join our organization with many companies prioritizing Next Generation Risk Assessment and few that deliver on its requirements. With a vision of being THE trusted source in chemical safety & assessment – while elevating human and environmental health and well-being, do you have what it takes to join our team and make your mark?
Job Overview:
We seek a research scientist to lead our Literature Review project, an initiative to modernize toxicology evidence gathering through automation and AI-assisted curation. The successful candidate will design and implement user-friendly tools and workflows that transform the ingestion, triage, and synthesis of scientific literature into streamlined, auditable processes that support developmental and reproductive toxicology (DART), IVIVE, and PBPK modeling. This role bridges information retrieval, natural language processing, and computational toxicology. It will deliver production-grade evidence pipelines that feed ScitoVation’s risk assessment and modeling products, including DRIIVE, our developmental PBPK/IVIVE platform, and deliver traceable, regulator-ready evidence pipelines that meet the highest standards of transparency and reproducibility.
You will develop robust systems that query public and proprietary bibliographic databases; extract, normalize, and annotate study details (chemicals, cell types, doses, endpoints, PK parameters); and produce traceable evidence maps and living reviews aligned with PRISMA and related best practices. The ideal candidate combines strong NLP/IR engineering skills with an understanding of toxicology and risk assessment needs and has a passion for building tools that make complex science reproducible, accessible, and regulator-ready. This interdisciplinary project offers an excellent opportunity to work at the cutting edge of alternative methods for safety assessment and to make a significant impact on regulatory science.
Accountable for:
- Designing and implementing an end-to-end platform that supports search strategy definition, deduplication, screening, study tagging, data extraction, and evidence mapping for DART and broader NAMs.
- Developing reusable ontologies, controlled vocabularies, and entity recognition components for chemicals, assays, endpoints, kinetics, and uncertainty constructs; aligning outputs with Reactome, GO, MeSH, and domain ontologies used in PBPK/IVIVE.
- Engineering production data pipelines to harvest and normalize literature from APIs and corpora (e.g., PubMed, Europe PMC, Crossref), parse PDFs and supplemental materials, and harmonize tabular andfigure-embedded data for downstream modeling.
- Engineering pipelines to harvest and normalize literature from APIs and corpora (e.g., PubMed, Europe PMC, Crossref), including PDF and supplemental data parsing.
- Building NLP-based ranking, triage, and semi-automated screening tools using modern approaches (transformers, retrieval-augmented generation, active learning).
- Ensuring all workflows are auditable, reproducible, and regulator-ready, with clear provenance and quality flags.
- Delivering user-friendly web interfaces (React/TypeScript preferred) that allow toxicologists to interact with the system efficiently.
- Integrating outputs with ScitoVation’s computational modeling efforts to supply curated kinetic priors and benchmark references.
- Authoring client-facing evidence summaries, gap analyses, and decision memos; contributing to publications and white papers on automated evidence synthesis in computational toxicology.
- Collaborating with computational modelers, wet-lab partners, and software engineers to ensure tools meet scientific requirements and operate reliably in cloud environments.
Education and Experience:
- PhD in computational biology, information science, computer science, biostatistics, or a related discipline; exceptional MS candidates with relevant experience will be considered.
- Demonstrated proficiency with Python and/or R for NLP/IR (e.g., Hugging Face Transformers, spaCy, scikit-learn, pandas, tidyverse), PDF parsing and table extraction, and data engineering.
- Experience designing systematic or rapid review workflows; familiarity with PRISMA, living reviews, and tools such as SWIFT-Review, DistillerSR, or EPPI-Reviewer is advantageous.
- Exposure to toxicology, pharmacology, or risk assessment contexts, particularly DART or NAMs, is strongly preferred; ability to communicate scientific nuance to non-experts is essential.
- Experience with cloud-based development (AWS/Azure/GCP), containerization, CI/CD, and reproducible research (git, data versioning) is a plus.
- Familiarity with front-end frameworks (JavaScript/TypeScript, React, basic data visualization) is a plus for building analyst-friendly screening interfaces.
Competencies:
- Outstanding scientific writing and structured communication; ability to translate complex evidence into clear, defensible summaries.
- Rigorous organization and attention to detail; comfort critiquing your own work and that of others.
- Time management and proactive risk mitigation; commitment to transparent documentation and QA.
- Collaborative mindset and enthusiasm for cross-disciplinary teamwork.
Position Type/Expected Hours of Work:
This is a full-time position, and hours of work and days are generally Monday through Friday, 9:00 a.m. to 5 p.m. Work outside of normal hours will sometimes be required.
Travel:
Travel is not expected in this position.
Supervisory Responsibilities:
This role does not have supervisory responsibilities.
Other Duties:
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice. The base pay range will reflect the anticipated base pay for this position if a selected candidate were to be located in North Carolina. Base pay may vary based on location and other factors.
Only candidates selected for an interview will be contacted.
Please submit cover letter and resume to rbarutcu@scitovation.com