Machine Learning Research Scientist (m/f/d)

Pfizer Pharma GmbH

  • Berlin
  • Post Date: 24. October 2024
Job Description

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 position:

Machine Learning Research Scientist (m/f/d)


2 year temporary contract

This role is part of the Pfizer Postdoc program

Pfizer's biomedical AI hub is seeking a passionate and creative machine learning researcher to design and implement new machine learning tools to accelerate drug discovery. The ideal candidate should have an outstanding scientific reputation in the field of machine learning research for generative chemistry and contrastive learning for drug discovery.

S/he will design new approaches to derive insights from Pfizer's proprietary data and external datasets to generate testable hypotheses across the drug discovery continuum. The individual will also design, establish, and manage internal and external collaborations to advance Pfizer's ML capabilities.


Principal downstream application domains include, indication expansion, target selection, disease mechanism elucidation, and patient selection, partnering with practitioners in these areas to advance the state-of-the-art.

  • Develop innovative machine learning approaches that leverage the plethora of Pfizer's proprietary data in conjunction with external data sources to advance drug discovery and identify novel disease treatment mechanisms
  • Develop a Pfizer internal knowledge-graph for internal and external biomedical data and predict new links for hypothesis generation
  • Advance the internal graph learning tech-stack to cope with, spurious correlation, obscuring variation and inherent multimodality of biomedical, chemical, and -omics data
  • Research, design and implement graph learning algorithms for analysis problems related to drug discovery and development
  • Be an active member of a highly interdisciplinary team
  • Conceive, execute and evaluate studies and experiments, interpret the results and present them to scientist in other functions
  • Generate direct impact on our discovery projects to help patients
  • Strengthen external visibility and scientific excellence through publishing / presenting work in reputed journals and conference/workshop venues and engaging with the scientific community


  • Formal training in Physics, Chemisty, Biology, Computational Biology, Statistics, related technical discipline.
  • PhD and relevant research experience in developing machine/deep learning-based solutions and a sincere interest for computational life sciences.
  • Hands-on experience in handling, processing, integrating, and analyzing large heterogenous data sets related to industrial drug discovery research with of one or more scientific data modalities
  • Proven expertise in developing machine learning for generative chemistry and contrastive learning
  • Highly creative person with outstanding problem-solving skills to tackle complex analysis tasks in a timely fashion.
  • Strong publication record and demonstrated contributions to the field
  • Solid expertise with ML libraries such as PyTorch, Lightning, TensorFlow is mandatory! Programming skills in Python must be top-notch. Experience with relevant libraries of the Python scientific stack is a big plus.
  • Familiarity with GPU computing both on-premises and on cloud platforms
  • Passion and curiosity for data and proven ability to take ideas from prototype to production.
  • Strong interpersonal skills, distinct collaborative attitude, excellent written and verbal communication

Breakthroughs that change patients' lives - In order to fulfill our corporate purpose, a value-oriented corporate culture guides our actions. Pfizer's values are: Courage, Excellence, Equity & Joy.

Courage: One bold way we are achieving our goals is our company-wide digital transformation strategy. Our flat hierarchies enable short decision-making paths.

Excellence: We focus on what is really important, take responsibility, measure progress and work together in a spirit of trust. Together we rely on an agile way of working that encourages our employees to balance their private and work lives and to promote personal development.

Equity: We believe that different experiences are valuable, which is why every opinion is heard and valued. These experiences and opinions enrich the entire company. In this way, we promote a diverse and inclusive working environment in which colleagues in various Diversity, Equity & Inclusion (DE&I) working groups such as, e.g. Engage Empowered Women, LGBT*IQ , DisAbility, X-Gen.

Joy: If we experience our work as meaningful, we get a lot in return. We achieve this by being proud of the contribution we make, appreciating each other and sharing this with joy and recognition. Our BRAVO Award program gives us an appreciative opportunity to do so. Our employees benefit from a comprehensive company health management "Pfizer in Balance" also during working hours.

Living our values extends well-beyond the workplace, you get the opportunity to support people in need and to carry out our efforts around diversity, inclusion & equity, environmental sustainability and against any form of exclusion through volunteering e.g., during our annual engagement days.

For us, it goes without saying that we offer fair remuneration in accordance with the IG BCE framework collective agreement, as well as a pension scheme and many other attractive benefits.

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