Senior Real World Data Scientist

Senior Real World Data Scientist / Epidemiologist / Biostatistician

Location: Kaiseraugst, Switzerland

About

Are you excited about scientific innovation and eager to provide quantitative advice and insights for our Human Nutrition and Care research and development? Do you enjoy working with large‑scale observational nutrition datasets (e.g. UK Biobank, NHANES) and applying cutting‑edge methods like causal inference in a real‑world context? Then this position might be the right next step for you. We are looking for an experienced and motivated colleague to join our biostatistics team in the Data Science department.

Key Responsibilities

• Provide statistical and epidemiological expertise on observational nutrition data. Analyze large cohort datasets, surveys, biobanks, digital biomarkers, consortium data and real‑world data to generate insights for better nutrition, healthy ageing and prevention. Apply advanced techniques such as causal inference to address complex questions in nutrition and health.
• Support our multidisciplinary project teams in Human Nutrition and Care with your statistical know‑how. This includes optimizing decision‑making in all discovery and development phases of our nutrition and care products by designing robust experiments and analyzing data from clinical trials or consumer studies.
• Integrate and analyze data from diverse sources (e.g. combining evidence from observational studies and randomized trials) to perform meta‑analyses and develop a comprehensive evidence base for product development and marketing claims in nutrition and care. Collaborate closely with bioinformaticians in the team who analyze related omics data.
• Optimize study design for human clinical studies or observational studies in collaboration with study directors and scientists, ensuring scientifically sound and statistically powered designs that can lead to robust results and actionable insights.
• Work closely and in a result‑oriented manner with colleagues across disciplines – bioinformaticians, study directors, nutritionists, project managers, data managers, marketing teams, etc. – either as a core project team member or as an ad‑hoc statistical consultant, to ensure data‑driven decision making.
• Participate in external collaborations on a global scale (with universities, research institutes, industry partners, CROs, etc.) to stay at the forefront of nutritional research and statistical methodologies and to drive innovation in how we use data in nutrition and personal care research, e.g. regarding causal inference and target trial emulation.
• Oversee the outsourcing of data analyses to external partners and ensure that all analyses meet our quality standards and adhere to relevant guidelines and regulations.
• Work on a broad variety of projects and questions and handle competing priorities in a dynamic environment.

We Offer

• A broad variety of projects, data types, and statistical approaches, ensuring you never stop learning and can apply a wide range of methods.
• A team of diverse, open‑minded colleagues who aren’t afraid to think outside the box and challenge the status quo.
• A truly global and collaborative team environment that cares about our employees’ experience and growth.
• The encouragement and support you need to develop professionally and achieve your goals.
• A caring and supportive workplace where you’re empowered to grow, share your ideas, and make a real impact.
• A safe, inclusive workplace where you feel welcome and respected.

You Bring

• A Master’s or preferably PhD degree in Epidemiology, Biostatistics, Data Science, or a related field, with several years of work experience (a PhD will be considered as work experience).
• Solid programming experience in R is essential; experience with programming in Python and ability to read SAS code would be a plus.
• Familiarity with large‑scale cohort data and the challenges of working with real‑world evidence (e.g. handling biases, missing data, etc.). Experience analyzing data from sources like national health surveys or biobanks or nutritional data is preferred.
• Sound knowledge of standard statistical methods, particularly regression and its extensions (e.g. GLMM), as well as experience with causal inference techniques and meta‑analysis methods. Experience with target trial emulation is a plus.
• Experience with machine learning, advanced analytics, data engineering/wrangling, database tools (SQL), cloud computing platforms (such as AWS), or version control (Git) is a plus.
• Experience with clinical trial data standards (e.g. CDISC) and knowledge of data privacy regulations (e.g. GDPR) is a plus.