A career at Pfizer offers opportunity, ownership and impact.

All over the world, Pfizer colleagues work together to positively impact health for everyone, everywhere. Our colleagues have the opportunity to grow and develop a career that offers both individual and company success; be part of an ownership culture that values diversity and where all colleagues are energized and engaged; and the ability to impact the health and lives of millions of people. Pfizer, a global leader in the biopharmaceutical industry, is continuously seeking top talent who are inspired by our purpose to innovate to bring therapies to patients that significantly improve their lives.

Right now, we are seeking highly qualified candidates to fill the following position in Berlin:

Senior Computational Biologist Genetics (m/f/d)

About the role:

Pfizer's cross-functional 'omics ecosystem team is actively seeking a highly motivated, creative, and experienced senior computational biologist specializing in genetics, particularly in the field of Genome-Wide Association Studies (GWAS). The selected candidate will play a pivotal role in our dynamic and pioneering cross-functional team, with a primary focus on advancing the development of a state-of-the-art genetics platform for Pfizer R&D across research areas. As a full-stack computational biologist, the candidate will utilize their expertise to design innovative approaches for extracting valuable insights from both Pfizer's proprietary data and external datasets, with a specific emphasis on GWAS and the motivation of new targets based on genetic evidence. This will involve developing and implementing cutting-edge methodologies to enable the generation of testable hypotheses throughout the drug discovery value chain.

Your responsibilities include:

• Development and implementation of a genetics data platform to enable efficient and scalable analysis of genetics data in order to provide evidence for new target, indications, safety

• Development of effective machine learning data loaders together with the machine learning team for cutting-edge single cell machine learning

• Processing and harmonization of internal GWAS studies

• Curation and integration of relevant datasets from the public domain, e.g. GWAS catalogue

• Development of a data dissemination concept for genetics data

• Integration of (public) data sources to contextualize genetics data

• Implementation of new methods and pipelines for genetics data analysis and visualization

• Conceptualization and implementation of a target prioritization platform

• Working out TREs.

• Drive collaborations with external companies

• Develop Pfizer genetics data strategy together with other functions from the research units.

• Strengthen external visibility and scientific excellence through publishing / presenting work in reputed journals and conference/workshop venues and engaging with the scientific community

Join us in this exciting opportunity to contribute to the forefront of bioinformatics research in genetics, GWAS, and make a significant impact on Pfizer's R&D efforts.

About you:

• Formal training in Computational Biology, Biology, Physics, Statistics, related technical discipline.

• PhD and several years of relevant research experience in developing genetics analysis and data integration methods and a sincere interest for computational life sciences.

• Highly creative person with outstanding problem-solving skills to tackle complex analysis tasks in a timely fashion.

• Strong full-stack developer with focus on python, in-depth database expertise with a focus on postgres, ETL frameworks.

• Exceptional programming skills in Python are a prerequisite. Familiarity with pertinent libraries within the Python scientific stack would be highly advantageous.

• Proficiency in utilizing ML libraries including PyTorch and Lightning is essential

• Proven expertise in software engineering, package development, cloud architectures, CI/CD and software engineering tooling

• Hands-on experience in handling, processing, integrating, and analyzing large heterogenous data sets related to genetics data in a drug discovery research environment

• Strong publication record and demonstrated contributions to the field

• Passion and curiosity for data and proven ability to take ideas from prototype to production.

• Strong interpersonal skills, distinct collaborative attitude, exc