Description
Real World Biostatistician - RWE CMH experience
Syneos Health® is a leading fully-integrated life sciences services organization built to accelerate customer success. We partner with innovators at every point across the drug development and commercialization continuum, helping them navigate complexity, anticipate change and accelerate progress.
Our Clinical Solutions team members act with a drug development mindset, applying their years of experience and deep expertise to truly understand customer needs and represent those in the solutions we shape.
Whether you join us in a Functional Service Provider partnership or a Full-Service environment, you’ll collaborate with passionate problem solvers, innovating as a team to help our customers achieve their goals. We are agile and driven to deliver – for one another, our customers, and, most importantly, for those in need.
Discover what your 25,000 future colleagues already know:
Why Syneos Health
- We are passionate about developing our people, through career development and progression; supportive and engaged line management; technical and therapeutic area training; peer recognition and total rewards program.
- We are committed to building an inclusive culture – where you can authentically be yourself. Central to this is our purpose – Driven to Deliver – which captures the passion of our colleagues to show up each day and shape solutions that have the ability to dramatically impact someone’s life.
- We are continuously building the company we all want to work for and our customers want to work with. Why? Because we know that when we bring together smart colleagues from across the world, we can shape the future of healthcare, driving impact for customers and defining the pace of patient progress.
Job Responsibilities
Biostatistician – Real-World Evidence (RWE)
Role Overview
We are seeking a biostatistician with strong experience in real-world data (RWD) with observational study design and safety study experience in addition to RWE CMH experience. This role will support evidence generation across multiple therapeutic areas. This role will focus on the design, analysis, and interpretation of observational studies using EMR and claims data to inform: clinical development, HEOR, regulatory strategy, and market access.
Key Responsibilities
Design and execute real-world evidence (RWE) studies using EMR and claims data; Conducting data specs, SAP and protocol with key research objectives
Develop and apply robust statistical methodologies, including:
Causal inference methods (e.g., propensity score methods, weighting, matching; GLM or GLMM, MMRM; survival analysis; random forest)
Trial emulation frameworks
External control arm development and borrowing strategies
Perform data analysis using healthcare coding systems (e.g., ICD, NDC)
Conduct sample size estimation and power calculations for observational and hybrid study designs
Collaborate cross-functionally with stakeholders across:
HEOR
Market Access
Regulatory
Clinical Development
Translate complex analytical results into clear, actionable insights, e.g. powerpoint or study report for decision-making
Support methodological innovation in RWE, including integration of machine learning approaches where appropriate
Required Qualifications
M.S. or Ph.D. in Biostatistics, Statistics, Epidemiology, or related field
- 5 years of experience in RWD/RWE analytics (industry or equivalent)
Strong experience with EMR and/or claims data
Proficiency in healthcare coding systems (e.g., ICD, NDC)
Programming expertise in at least one of: SAS, R, or Python
Working knowledge with SQL logic and OMOP data structures
Solid understanding of:
Causal inference methods
Observational study design
Sample size and power considerations
Some examples: Independently write cohort definitions in SQL logic; Debug data issues e.g., time zero alignment, exposure gaps; Understand concept mapping (ICD SNOMED RxNorm); Translate statistical estimand censoring rule and data extraction logic
RWE CMH Experience
Preferred Qualifications
Ph.D. strongly preferred
Experience in one or more therapeutic areas:
Diabetes
Cardiovascular disease
Metabolic disorders
Familiarity with:
Trial emulation methodologies
External control borrowing / hybrid designs
Basic machine learning methods applied to RWD
Demonstrated ability to work across multiple therapeutic areas (TAs) in a fast-paced environment
Strong communication and stakeholder engagement skills
Advanced (nice-to-have, not always required)
Build reusable cohort pipelines
Optimize queries for large-scale databases
Work across multiple CDMs (OMOP, Sentinel, PCORnet)
Core Competencies
Analytical rigor and methodological depth
Cross-functional collaboration
Ability to operate with agility across diverse projects and therapeutic areas
Clear and effective scientific communication
Get to know Syneos Health
Over the past 5 years, we have worked with 94% of all Novel FDA Approved Drugs, 95% of EMA Authorized Products and over 200 Studies across 73,000 Sites and 675,000+ Trial patients.
No matter what your role is, you’ll take the initiative and challenge the status quo with us in a highly competitive and ever-changing environment. Learn more about Syneos Health.
http://www.syneoshealth.com
Additional Information
Tasks, duties, and responsibilities as listed in this job description are not exhaustive. The Company, at its sole discretion and with no prior notice, may assign other tasks, duties, and job responsibilities. Equivalent experience, skills, and/or education will also be considered so qualifications of incumbents may differ from those listed in the Job Description. The Company, at its sole discretion, will determine what constitutes as equivalent to the qualifications described above. Further, nothing contained herein should be construed to create an employment contract. Occasionally, required skills/experiences for jobs are expressed in brief terms. Any language contained herein is intended to fully comply with all obligations imposed by the legislation of each country in which it operates, including the implementation of the EU Equality Directive, in relation to the recruitment and employment of its employees. The Company is committed to compliance with the Americans with Disabilities Act, including the provision of reasonable accommodations, when appropriate, to assist employees or applicants to perform the essential functions of the job.
Summary
Job Description Summary Biostatistician – Real-World Evidence (RWE) Role Overview We are seeking a biostatistician with strong experience in real-world data (RWD), observational study design, and RWE CMH to support evidence generation across multiple therapeutic areas. This role will focus on the design, analysis, and interpretation of observational studies using EMR and claims data to inform: clinical development, HEOR, regulatory strategy, and market access. Key Responsibilities Design and execute real-world evidence (RWE) studies using EMR and claims data; Conducting data specs, SAP and protocol with key research objectives Develop and apply robust statistical methodologies, including: Causal inference methods (e.g., propensity score methods, weighting, matching; GLM or GLMM, MMRM; survival analysis; random forest) Trial emulation frameworks External control arm development and borrowing strategies Perform data analysis using healthcare coding systems (e.g., ICD, NDC) Conduct sample size estimation and power calculations for observational and hybrid study designs Collaborate cross-functionally with stakeholders across: HEOR Market Access Regulatory Clinical Development Translate complex analytical results into clear, actionable insights, e.g. powerpoint or study report for decision-making Support methodological innovation in RWE, including integration of machine learning approaches where appropriate Shape Required Qualifications M.S. or Ph.D. in Biostatistics, Statistics, Epidemiology, or related field 5 years of experience in RWD/RWE analytics (industry or equivalent) Strong experience with EMR and/or claims data Proficiency in healthcare coding systems (e.g., ICD, NDC) Programming expertise in at least one of: SAS, R, or Python Working knowledge with SQL logic and OMOP data structures Solid understanding of: Causal inference methods Observational study design Sample size and power considerations Some examples: Independently write cohort definitions in SQL logic; Debug data issues e.g., time zero alignment, exposure gaps; Understand concept mapping (ICD SNOMED RxNorm); Translate statistical estimand censoring rule and data extraction logic Shape Preferred Qualifications Ph.D. strongly preferred Experience in one or more therapeutic areas: Diabetes Cardiovascular disease Metabolic disorders Familiarity with: Trial emulation methodologies External control borrowing / hybrid designs Basic machine learning methods applied to RWD Demonstrated ability to work across multiple therapeutic areas (TAs) in a fast-paced environment Strong communication and stakeholder engagement skills RWE CMH Experience Advanced (nice-to-have, not always required) Build reusable cohort pipelines Optimize queries for large-scale databases Work across multiple CDMs (OMOP, Sentinel, PCORnet) Shape Core Competencies Analytical rigor and methodological depth Cross-functional collaboration Ability to operate with agility across diverse projects and therapeutic areas Clear and effective scientific communication
