Senior Statistician – Real World Data Analytics Consultant

 

Are you interested in working directly for a single sponsor while having the security and additional career opportunities that working for a global CRO can bring? Our team says it’s the best of both worlds….

ClinChoice is searching for a Senior Statistician – Real World Data Analytics Consultant to join one of our clients. 

ClinChoice is a leading global CRO, dedicated to supporting clinical trials and real-world evidence research with a focus on exceptional quality, career development, and a supportive culture. As we expand our presence and offerings, we’re looking for an experienced Senior Statistician – Real World Data Analytics Consultant to join our dynamic team and lead analytical efforts across diverse therapeutic areas for a high-profile sponsor.

Position Overview: The Senior Statistician will play a key role in analyzing complex real-world healthcare data and providing statistical support for observational research. Working alongside our Senior Real World Data Analytics Consultant, this individual will contribute to the design, analysis, and interpretation of real-world evidence (RWE) studies across various therapeutic areas. This role involves working directly with clients, collaborating with cross-functional teams, and ensuring the highest quality of statistical methodologies.

Key Responsibilities:

• Lead the statistical design and analysis of complex observational studies. Provide expertise on the selection of appropriate statistical methods for real-world data, including propensity scores, regression analysis, survival analysis, and longitudinal data analysis.
• Work closely with the Senior Real World Data Analytics Consultant and other cross-functional team members (data scientists, epidemiologists, and analysts) to ensure that statistical methods align with research objectives. Mentor junior statisticians and analysts, providing guidance on statistical methodology and analysis techniques.
• Engage with clients to discuss statistical approaches, provide insights into analysis results, and answer statistical questions. Present findings clearly and effectively to non-statistical stakeholders.
• Work on the development of statistical analysis plans (SAPs), including determining statistical methods for data analysis, sample size calculations, and handling of missing data. Ensure methodologies are aligned with regulatory standards and industry best practices.
•  Review statistical outputs, ensure accuracy and consistency, and validate statistical findings. Work with the team to ensure the highest quality of statistical work.
•  Prepare statistical reports, summaries, and presentations of results. Communicate complex statistical concepts and findings to diverse stakeholders, including clients, regulatory agencies, and internal teams.
• Stay up to date with the latest developments in statistical methods and data science techniques. Contribute to the improvement of statistical workflows, tools, and processes to enhance efficiency and quality.
• Collaborate with data scientists, bioinformaticians, and clinical research teams to integrate and analyze large, complex datasets. Support database management and contribute to the development of automated analytic tools.

Qualifications:

• Experience:
  • 6-8 years of experience as a statistician in real-world data analytics, biostatistics, or a related field, with a strong focus on health outcomes and observational studies.
  • Experience in working with large healthcare databases (e.g., claims data, EMR data, registry data).
  • Proven track record of statistical analysis in the life sciences, pharmaceutical, or healthcare industries, including regulatory submissions and clinical trials.
• Technical Skills:
  • Proficient in statistical software such as SAS, R, or similar platforms.
  • Experience with SQL, Python, or other programming languages is preferred.
  • Knowledge of advanced statistical techniques such as survival analysis, regression modeling, mixed-effects models, and propensity score matching.
  • Familiarity with data visualization tools such as Tableau, Power BI, or R/Shiny is an advantage.
• Knowledge & Expertise:
  • In-depth understanding of epidemiological study design, statistical modeling, and data analysis techniques in real-world evidence studies.
  • Experience