Data Scientist - Remote, USA

Calyxo

3d•$135,000 - $155,000•Remote

About The Position

Calyxo, Inc. is seeking a Data Scientist to support evidence generation across the medical device product lifecycle. Under the direction of the Manager of Biostatistics & Data Science, this role transforms real-world, clinical, and preclinical data into reliable, reproducible datasets, analyses, and insights for research and cross-functional business needs. The role supports real-world data initiatives and statistical execution of preclinical and clinical studies, including study design, endpoint definition, analysis planning, statistical programming, data review, interpretation, and reporting. The Data Scientist works with Scientific, Clinical, R&D, Regulatory, Quality, IT, and external data partners. The position requires technical rigor, sound judgment, clear documentation, and the ability to explain analytical results to technical and nontechnical audiences. The position is remote in the United States and requires travel up to 10% of the time. Calyxo describes its team as knowledgeable, high-achieving, experienced, and fun, and offers a diverse work environment with experienced leaders and opportunities to contribute to company culture. The company emphasizes learning, dynamic challenges, diversity, and inclusion. Compensation includes a base salary of $135,000–$155,000 and a variable incentive plan, stock options, and an employee benefits package that includes 401(k), healthcare insurance, and paid vacation.

Requirements

Bachelor’s degree in data science, statistics, biostatistics, computer science, epidemiology, public health, or a related quantitative field, plus 7 years of relevant experience; or a master’s degree in a related field, plus 5 years of relevant experience.
Practical experience using R to prepare, analyze, visualize, and validate complex datasets.
Experience translating a research question into a well-defined dataset, analysis plan, and transparent, reproducible analytical workflow.
Experience with statistical analysis of clinical, preclinical, observational, or other health-related data.
Working knowledge of study design, endpoint definition, analysis planning, and interpretation of clinical or preclinical study data.
Ability to develop, review, and document statistical programs and analytical outputs with appropriate quality control and traceability.
Strong data-quality, documentation, problem-solving, and communication skills.
Ability to work independently while collaborating effectively in a cross-functional team environment.
Legal authorization to work in the United States is required; hires must verify identity and eligibility to work in the United States.

Nice To Haves

Master’s degree or PhD in biostatistics, statistics, epidemiology, data science, or a related quantitative field.
Familiarity with SAS, Python, and SQL.
Experience with real-world data, EHR records, claims data, clinical trial data, or other longitudinal healthcare data.
Familiarity with observational study methods and real-world evidence generation.
Experience with clinical text, natural language processing, regular expressions, or clinical concept extraction.
Experience supporting preclinical or clinical study design, statistical analysis plans, sample size or precision calculations, or statistical reporting.
Experience creating Tables, Listings, and Figures or contributing to clinical study reports, publications, abstracts, posters, or regulatory submissions.
Experience working in a medical-device, clinical-research, pharmaceutical, or other regulated environment.
Familiarity with applicable clinical research and data standards, including GCP, ISO 14155, ICH E9, and relevant FDA guidance.
Passion for turning complex data into reliable evidence that supports better decisions and patient impact.
Solution-focused mindset with the ability to anticipate challenges and propose pragmatic, high-quality approaches.
Careful attention to detail and commitment to reproducibility, transparency, and scientific rigor.
Curiosity and adaptability across data science, biostatistics, clinical research, and real-world evidence.
Ability to communicate clearly, collaborate generously, and contribute to shared team outcomes.

Responsibilities

Translate research and business questions into cohort definitions, data requirements, analytic plans, and clear deliverables.
Assess the availability, provenance, quality, and suitability of real-world data, EHR records, claims data, clinical data, and other relevant data sources.
Build, validate, and maintain reproducible workflows for data extraction, transformation, cohort construction, data-quality checks, and analysis-ready datasets.
Develop, validate, and document workflows for structured clinical data and unstructured notes, including clinical concept extraction and natural language processing where appropriate.
Support real-world evidence and post-market analyses, including trend and subgroup assessments, evidence synthesis, and analyses evaluating device safety and performance.
Provide biostatistical support for preclinical and clinical studies, including input on protocols, study design, endpoints, analysis populations, sample size or precision considerations, and data-collection strategies.
Support development and maintenance of Statistical Analysis Plans, programming specifications, analysis datasets, table shells, and other analysis documentation.
Review study data and data structures for quality, completeness, consistency, and alignment with planned analyses; identify issues and help coordinate resolution.
Perform and quality-check descriptive and inferential analyses, including interim and final analyses, sensitivity analyses, subgroup analyses, and other study-specific analyses using reproducible and traceable practices.
Interpret and communicate results in the context of study objectives, limitations, and clinical or scientific relevance.
Contribute to study reports, clinical evaluation materials, regulatory submissions, publications, abstracts, posters, and presentations.
Build and implement data workflows with internal IT and external data partners, following processes for access, secure storage, audit logging, permitted use, onboarding, and data transfer.
Document data definitions, source systems, transformations, statistical methods, assumptions, quality checks, and results so work can be reviewed and reproduced.
Apply quality-control practices to datasets, analyses, programs, and outputs, and escalate material issues and risks in a timely manner.
Contribute to development and continuous improvement of standards, templates, workflows, and best practices for data science and biostatistical analyses.
Explain complex statistical and data-science concepts clearly to technical and nontechnical stakeholders.
Present analytical findings, assumptions, limitations, and recommendations to project teams and other internal audiences.
Contribute to a collaborative, accountable, and scientifically rigorous team environment.
Travel up to 10% of the time.