Job DescriptionJob Purpose The RWE Scientific Data Analysts I will gain experience conducting Real World Evidence (RWE) studies (e.g. predictive health outcomes modeling) and interactive dashboards (e.g. for disease exploration) using large observational databases (e.g. medical and pharmacy claims data, electronic health records) in support of Novartis RWE strategies, improving our understanding of human diseases and improving patient health. Major Accountabilities • Develop, modify and de-bug programming routines (such as SAS or R) along with SQL queries used to extract, clean, manage, and analyze large databases for health outcomes research under senior guidance. Data sources include medical and pharmacy claims data, hospital data, electronic medical record data, and prospective observational study data.• Translate the study design into algorithms to extract, analyze and report secondary data for Non-Interventional Studies (NIS) or interactive data visualization tools under senior guidance.• Gain competence with core Real World Evidence (RWE) tools such as R, R/shiny, SAS, Impala SQL, git or JIRA.• Contribute to RWE dashboard, NIS and predictive analytics projects as required.• Gain competence with core machine learning and data mining techniques such as random forest, GBM, logistic regression, SVM, deep learning.• Basic knowledge in dimensional reduction and R packages such as ggplot, plotly or t-sne.• Maintain consistency and comply with company standards of the project documentation for observational studies and interactive data visualization tools, including programming, specifications of analysis datasets, tables, figures and listings under senior guidance.• Conduct quality review in compliance with company standards and processes upon various stages of the projects under senior guidance.• Support and contribute to standardization techniques in order to increase efficiency in deliverables under senior guidance.• Comply with project timelines together with the RWE Research Analyst and proactively alert the project manager or department leadership on any risks related to the deliverables and the timeline.• Attend meetings with customer on a regular basis. Diversity & Inclusion / EEO Minimum Requirements Education:Undergraduate degree in a field such as computer science, bioinformatics, biostatistics, statistics or similar. No prior industry experience required. Language:English fluent (oral and written) Minimum Requirements:• Basic understanding in R, SAS or another statistical programming language.• An interest in developing programming, statistical, visualization and analytical skills.• Exceptional problem-solving abilities with a basic understanding of statistical methods (regression, Anova, t-tests, etc.).• Ability to prioritize tasks and communicate overall timelines in order to efficiently produce high quality deliverables.• Strong written/verbal communication skills. Highly effective at providing input at meetings, discussions and activities covering aspects of research data management and analysis on research projects.• A strong interest in working in pharma.• Open to experimentation and taking smart risks to support creative thinking that leads to practical solutions to healthcare and business challenges.• Holds a high standard on quality excellence. Continuously seeking to enhancing standards, technology through expansion of knowledge and training.• Support teamwork to swiftly and efficiently deliver innovative new products to patients and healthcare providers.• High ethical values and standards.• Able to speak out, challenge conventional thinking, and stand up for ideas.• Strong team spirit.• Ability to work well within a global organization.Preferred Qualifications:• Familiarity with visualization and dimensional reduction techniques.• Familiarity with version control tools such as git or JIRA.• Familiarity with technologies for analyzing big data.• Prior work placement/internship experience with pharma/biotech/RWE organizations.• An interest in pursuing advanced education in the areas of biostatistics, epidemiology or data science.