Act as Program statistician for several biosimilar development programs within cross-functional global development teams;
Provide scientific, strategic and statistical input to clinical development strategies and trial designs for biosimilar development programs (including Biomarker, PK, PK/PD and efficacy/safety studies);
Champion, drive and implement innovative efficient trial designs and novel statistical methods within programs;
Independently lead interactions with Health Authorities (e.g. EMA, FDA, PMDA, NMPA) and key opinion leaders;
Lead planning and execution of innovative statistical analyses and eCTD preparation for regulatory submission and post-marketing activities including publications;
Be responsible for Sponsor oversight of trial-related activities performed by CROs/external partners;
Establish and maintain collaborative working relationships and effective communication within Biostatistics department, Sandoz BCD, Global Program Teams, as well as with Novartis Advanced Methodology, Novartis CD&A;
Explain statistical concepts in an easily understandable way to non-statisticians and provide relevant statistical interpretation and justification of analysis results;
Contribute to Due Diligence assessments for new product candidates;
Represent Sandoz in statistical forums/presentations/discussions at external congresses, conferences or scientific meetings.
What you’ll bring to the role:
PhD, PharmD or MS with honors in biometrics, statistics, mathematics, pharmacometrics, clinical pharmacology, or other quantitative discipline preferred;
PhD with 6+ years’ experience preferred OR MS with 10+ years’ experience;
Extensive knowledge and evidence of hands-on experience in at least 3 of the following key areas in industry and/or academia:
Design of clinical development programs (including Biomarker, PK, PK/PD and efficacy/safety studies);
Clinical efficacy equivalence trials;
Adaptive designs and clinical trial simulation;
Application of clinical pharmacology and/or pharmacometrics methods;
Bayesian trial design and analysis;
Advanced statistical modelling (e.g. evaluation of separate treatment effects in combination treatment regimens);
Application of Real World Evidence and machine learning in clinical trials.
Proficiency in use of statistical software packages (e.g. SAS, R);
Clinical, pharmacological and therapeutic knowledge;
Good interpersonal and communication skills (verbal and writing) to bridge scientific and business needs, integrating quantitative sciences.