Experienced Assistant or Associate Professor of Biostatistics

Statistics Interest Group; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Albert Einstein College of Medicine seeks an Experienced Assistant or Associate Professor of Biostatistics to collaborate with investigators on clinical and translational research. The role includes opportunities to teach biostatistics in medical and graduate training programs and to pursue an independent program of methodologic research.

The successful candidate will join a diverse, collegial environment with potential to develop an active research portfolio, publish peer-reviewed work, and

Qualifications

• PhD in biostatistics or statistics required, with strong publications.
• Experience in methodologic and collaborative research is preferred.
• Excellent oral and written communication skills are required.

Responsibilities

• Collaborate with Einstein investigators on clinical and translational research.
• Teach biostatistics in medical and graduate training programs.
• Pursue an independent program of methodologic research.

Skills

Biostatistics

Statistical methods

Collaborative research

Communication skills

Teaching

Education

Doctorate in biostatistics or statistics

Job description

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Albert Einstein College of Medicine – Experienced Assistant or Associate Professor of Biostatistics

Position Title Experienced Assistant or Associate Professor of Biostatistics

Company Information

Duties and Responsibilities

The successful candidate will collaborate with Einstein investigators on a wide range of clinical and translational research. Additionally, the successful candidate will have opportunities to teach biostatistics in the institution’s medical and graduate school training programs and to pursue an independent program of methodologic research. The position will offer diverse research experiences in a highly collegial environment.

Position Qualifications

Applicants must have a doctorate in biostatistics or statistics, a record of methodologic and collaborative research including peer-reviewed publications, relevant teaching experience, and excellent oral and written communication skills are required. Candidates with several years of post-training academic or professional experience, as well as expertise in one or more of the following areas are preferred: machine learning/artificial intelligence, causal inference, Bayesian methods, clinical trial methodology, analysis of electronic health record and other real-world data.