Work Schedule

Environmental Conditions

Job Description

As part of the Thermo Fisher Scientific team, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world’s toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer.

DESCRIPTION:

The Data Analyst I provides extensive statistical and data programming support across multiple projects within the Patient-Centered Research (PCR) group. The PCR group largely conducts survey (questionnaire) research with sample sizes ranging from quite small (less than 50 patients) to thousands of patients. You would be joining an industry-leading team of 20+ analysts dedicated to PCR science and covering a wide range of projects (e.g., statistical analysis of clinical endpoints, psychometric analysis of patient-reported outcome instruments, discrete choice modelling of patient preferences). The Data Analyst I will primarily conduct descriptive analyses and statistical modelling of clinical outcome assessment data from clinical studies. This role is integral to the success of the organization and works closely with a variety of stakeholders.

Essential Responsibilities:

• Manage and analyze research data

• Review and provide input into statistical analysis plan (SAP), study reports, and other relevant documentation for internal or external communication

• Attend regular meetings with research teams

Required skills:

• Master's degree or equivalent degree in Epidemiology, Health Economics, Biostatistics or related field, or BA/BS degree with at least 3 years of work experience

• Minimum of 3 years of SAS and/or R programming experience

• Strong written and oral communication skills in English

• Advanced knowledge in several of the following methods: Statistical testing (eg t-test, chi-2 test) and analysis of variance/covariance (ANOVA/ANCOVA); Exploratory/confirmatory factor analysis (EFA/CFA); Mixed effects modelling; Various regression techniques (eg linear, logistic, multinomial, ordinal, Poisson); Survival analysis (time-to-event data); Item-response theory (IRT); Data imputation; Multiple testing; Latent class analysis.

Desired skills:

• Work experience with healthcare or clinical-trial data

• Experience of data management following CDISC standards (eg, SDTM, ADaM)