Data Scientist - Vancouver, Canada - AbCellera Biologics

AbCellera Biologics
AbCellera Biologics
Verified Company
Vancouver, Canada

2 weeks ago

Sophia Lee

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Sophia Lee

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Description

AbCellera is a young, energetic, and rapidly growing tech company with an amazing team that searches, decodes, and analyzes natural immune systems to find antibodies that its partners can develop into drugs to prevent and treat disease.

We are seeking a highly motivated Data Scientist - Applied Statistics to support our rapidly growing functional and translational biology teams.


How you might spend your days:

  • Developing and implementing statistically grounded analytical methods for a variety of medium and high throughput experiments, including flow cytometry, spectrophotometry, spectrofluorimetry and high-content imaging.
  • Coordinating with other data scientists and software engineers to integrate these methods into automated analytical pipelines (typically in Python).
  • Collaborating with scientists to develop standardized wetlab and data analysis protocols.
  • Coordinating with different scientific, business and software teams to develop strategies for data analysis, curation, storage, presentation & interpretation.
  • Analyzing and presenting data and analysis methods to colleagues and partners.
  • Organizing, supporting and collaborating with other team members to meet project deliverables and timelines.

We'd love to hear from you if:

  • You are passionate about data science and want to work in an interdisciplinary environment.
  • You have a proven track record of developing statistical models that enable interpretation of complex data.
  • You have experience with probabilistic programming frameworks, such as Pyro or PyMC.
  • You enjoy and are able to effectively communicate complex technical concepts across disciplines and levels of experience. You enjoy cutting to the heart of a problem to explain it and explore solutions.
  • You take pride in being selfmotivated, a fast learner, a team player and a creative problem solver.


We don't expect all of these to match but we'll be excited to hear about the experiences that resonate with you.


Desired qualifications and experience:


  • PhD, MSc or BSc in statistics, computer science, computational biology or a related field with 3+ years of related work experience.
  • Strong fluency in a programming language for data analysis and an interest in using Python and the related data science stack (such as numpy, scipy, pandas) daytoday.
  • Expertise with a variety of statistical modeling techniques, such as Bayesian modeling, mixedeffects models, and nonlinear curve fitting.
  • Experience collecting or analyzing laboratory datasets.
  • Demonstrated ability to develop standardized analytical methods that can be used to interpret complex, multifactorial raw data.

About AbCellera:

At AbCellera, we find the next generation of therapeutic antibodies.

To succeed, we create innovative solutions - combining custom antigen preparation and immunizations, microfluidics, high-throughput imaging, genomics, computation, machine learning and laboratory automation - leaving no stone unturned, revolutionizing how scientists can explore natural antibody diversity and the scale at which they can do it.

You'll join a diverse and multi-disciplinary team of biologists, biochemists, engineers, bioinformaticians, computer scientists and physicists - all working together to bring better therapies to patients.

This is life-changing research and you could be a part of it.


We're a rapidly growing publicly traded company, and we have state-of-the-art discovery tools and the drive to be the best in the industry.

This isn't just about having the best technology. We know we need a world-class team of innovators working together to solve the toughest problems. We look for people with drive and energy. Idealists. People we love and people we trust. This may be unconventional, but it is the key to our success. We're looking for someone like you to support and organize our efforts along the way.


To apply:

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