- Страна
- США
- Зарплата
- 140 250 $ – 200 025 $
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Senior Clinical Informaticist
Отличная вакансия в передовой биотех-компании с четко прописанными обязанностями и конкурентной зарплатой. Удаленный формат работы, работа с современным стеком (AI, OHDSI) и социально значимая миссия делают это предложение крайне привлекательным для опытных специалистов.
Сложность вакансии
Высокая сложность обусловлена необходимостью глубоких знаний на стыке медицины и ИТ: владение OMOP CDM, специфическими онтологиями (SNOMED, LOINC) и навыками программирования на Python/SQL. Требуется опыт работы с реальными клиническими данными (EHR, claims) от 3 до 7 лет и понимание современных ИИ-методов в биоинформатике.
Анализ зарплаты
Предлагаемая зарплата ($140k - $200k) полностью соответствует рыночным ожиданиям для позиции Senior Clinical Informaticist в США, особенно в секторе Biotech/HealthTech. Верхняя граница диапазона даже несколько превышает медиану по рынку для удаленных ролей, что подчеркивает высокую ценность узкой специализации.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Clinical Informaticist position at Freenome. With extensive experience in clinical informatics and a deep understanding of real-world data (RWD) ecosystems, I am excited about the opportunity to contribute to your mission of early cancer detection. My background in developing computable cohorts and managing complex semantic normalization processes aligns perfectly with Freenome’s focus on building scalable, clinically rigorous data pipelines.
Throughout my career, I have gained hands-on experience with the OMOP common data model and the OHDSI toolset, which I see are central to this role. I have a proven track record of mapping diverse clinical terminologies such as SNOMED, LOINC, and RxNorm, and I am particularly enthusiastic about your initiative to integrate AI-enabled tools for clinical data extraction and phenotyping. My proficiency in SQL and Python, combined with my experience in EHR and claims data transformation, positions me to make immediate contributions to the RWD team.
I am impressed by Freenome’s commitment to leveraging advanced analytics and machine learning to improve patient outcomes. I am eager to bring my expertise in clinical ontologies and data curation to help ensure the accuracy and traceability of your research datasets. Thank you for considering my application; I look forward to the possibility of discussing how my skills can support Freenome’s innovative work.
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Описание вакансии
About this opportunity:
At Freenome, we are seeking a Senior Clinical Informaticist to help grow the Freenome Real-World Data team. The ideal candidate brings deep experience working with EHR, claims, and other real-world data sources, along with strong knowledge of clinical ontologies, vocabularies, and common data models. In this role, you will help design and maintain computable cohorts, semantic normalization processes, and proprietary ontologies that power Freenome’s research datasets. You will also play a key role in evaluating and integrating AI-enabled tools to enhance clinical data extraction, concept identification, and phenotyping workflows, including emerging capabilities from the OHDSI community. You will help ensure Freenome’s real-world data pipelines remain scalable, clinically rigorous, and ready to support advanced analytics and machine learning.
The role reports to the Senior Manager, RWD. This role will be a remote.
What you’ll do:
- Monitor, evaluate, and implement emerging AI-enabled tools and methodologies from the Observational Health Data Sciences and Informatics (OHDSI) community, supporting the responsible integration of these capabilities into the real-world data pipeline and cohort development workflows.
- Develop and maintain computable cohort definitions and longitudinal patient datasets from claims and EHR/EMR systems within scalable real-world data pipelines, leveraging AI-driven clinical data extraction and phenotyping approaches to identify patient populations for research and model development.
- Establish evaluation and validation strategies for AI-extracted clinical variables and cohorts.
- Manage and evolve the company’s proprietary semantic content, ontologies, and vocabularies, ensuring consistent interpretation and normalization of clinical concepts across the data pipeline.
- Support semantic normalization and quality control across the entire curation lifecycle to ensure consistent, standardized outputs.
- Design and implement scalable workflows for extracting and transforming clinical data from claims and EMR/EHR systems.
- Help enhance the end-to-end real-world data curation pipeline with AI-assisted medical record extraction, annotation, and curation techniques..
- Help automate clinical concept extraction, entity recognition, and phenotype detection with emerging AI methods.
- Collaborate with clinical scientists to ensure cohorts reflect clinically meaningful inclusion and exclusion criteria.
- Apply and map clinical terminologies including ICD-10, SNOMED, LOINC, CPT, RxNorm, and UMLS, seeking efficiency gains with AI methods
- Ensure clinical accuracy, traceability, and reproducibility of derived datasets.
- Support HIPAA-compliant data use, de-identification, and regulatory readiness for research applications.
Must haves:
- Advanced degree in Clinical Informatics, Biomedical Informatics, Medicine, Nursing, Public Health, or related field (MS, PhD, MD, RN, NP, PharmD, etc.).
- 3–7+ years experience working with clinical or real-world healthcare data.
- Hands-on experience with claims and EHR/EMR data extraction and transformation.
- Experience defining and building clinical cohorts or computable phenotypes.
- Strong knowledge of clinical coding systems (ICD-10, SNOMED, LOINC, CPT, RxNorm, etc.).
- Exposure to or familiarity with AI-assisted clinical data extraction, entity recognition, or phenotyping workflows.
- Experience with the OMOP common data model.
- Experience with SQL and Python.
Nice to haves:
- Experience with terminology browsers and authoring tools; OHDSI tools/Usagi preferred.
- Experience with cloud platforms; GCP, Azure, etc.
- Experience working with an agile-based team.
Benefits and additional information:
The US target range of our base salary for new hires is $140,250 - $200,025. You will also be eligible to receive pre-IPO equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered. Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @ freenome.com/job-openings/ for additional company information.
Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
Applicants have rights under Federal Employment Laws.
- Family & Medical Leave Act (FMLA)
- Equal Employment Opportunity (EEO)
- Employee Polygraph Protection Act (EPPA)
#LI-REMOTE
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Навыки
- SQL
- Python
- OMOP
- OHDSI
- SNOMED
- LOINC
- ICD-10
- RxNorm
- UMLS
- CPT
- GCP
- Azure
- Clinical Informatics
- EHR
- EMR
Возможные вопросы на собеседовании
Проверка практического опыта работы с основным стандартом данных, указанным в вакансии.
Опишите ваш опыт работы с моделью данных OMOP. С какими основными трудностями вы сталкивались при маппинге локальных данных в этот стандарт?
Оценка навыков работы с клиническими словарями, что критично для нормализации данных.
Как вы подходите к разрешению конфликтов при маппинге терминологий между ICD-10, SNOMED и RxNorm в рамках одного набора данных?
Вакансия делает упор на использование ИИ для автоматизации процессов.
Какие методы ИИ или NLP вы считаете наиболее эффективными для извлечения сущностей (entity recognition) из неструктурированных медицинских записей?
Проверка понимания методологии формирования выборок для исследований.
Расскажите о процессе разработки и валидации вычислимого фенотипа (computable phenotype). Как вы обеспечиваете его клиническую точность?
Работа с медицинскими данными требует строгого соблюдения этических и правовых норм.
Каков ваш опыт в обеспечении деидентификации данных в соответствии с требованиями HIPAA при подготовке датасетов для машинного обучения?
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- Страна
- США
- Зарплата
- 140 250 $ – 200 025 $