Real World Biostatistician - Safety Experience (hiring in US and Canada)

Real World Biostatistician - Safety Experience (hiring in US and Canada)
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

Job Description 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 Safety 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:

HEORMarket Access Regulatory Clinical  Development Translate complex analytical results into clear, actionable insights, e.g. power point 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 field5 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.

Preferred Qualifications Ph.D. strongly preferred

Familiarity with:

Trial emulation methodologies

External control borrowing / hybrid designs

Basic…