Data Scientist

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, well-versed in the analysis of complex cell biology data, to join our dynamic Discovery team.

**How you might spend your days**:

- Coordinating with different functional teams, with an emphasis on Discovery and Data Science, to develop strategies for data analysis, curation, storage, presentation and interpretation
- Building automated analysis pipelines for a variety of data types derived from medium
- and high-throughput experiments, including flow cytometry, spectrophotometry, spectrofluorimetry and high-content imaging
- Implementing and maintaining these analysis pipelines (typically in Python), ensuring data integrity, correctness and clarity
- Developing and implementing software solutions for improving data flow and maintaining data standards across multiple teams
- Organizing, supporting and collaborating with other team members to meet project deliverables and timelines
- Analyzing and presenting data and analysis methods to colleagues and partners

**We'd love to hear from you if**:

- You are passionate about data science and want to work in an interdisciplinary environment
- You thrive in a collaborative environment
- You enjoy bringing out the best in your teammates
- You enjoy working in a fast-paced work environment with multiple priorities
- You take pride in being self-motivated, a fast learner, a team player and a creative problem solver
- You can effectively communicate scientific ideas across disciplines and levels of experience

**Required qualifications and experience**:

- MSc in bioinformatics or computational biology with two or more years of related work experience, or BSc in a relevant field with five or more years of related experience
- Proficiency in at least one programming language for analysis and data visualization (Python & SQL preferred)
- Background in life sciences: Cell Biology, Immunology, or Molecular Biology preferred
- Demonstrated ability to create flexible data analysis pipelines intended for use by others, such as:

- Designing standardized statistical analysis and visualizations of complex multifactorial data
- Designing data analysis pipelines integrating multiple data sources, using tools such as Redshift & Bigquery or Spark & Dask
- Excellent communication skills, and the ability to identify key issues and succinctly describe them
- Driven to learn and master new technical skills
- Excellent documentation and organizational skills
- Impeccable attention to detail

**Offers & benefits**:

- The opportunity to work with an inspired team on challenging problems that matter
- An attractive compensation package, including health and lifestyle benefits
- A minimum of 3 weeks of vacation
- Opportunities for personal and professional development

**About AbCellera**:
At AbCellera, we’re solving tough problems and creating innovative solutions from the ground up - custom immunizations, microfluidics, high-throughput imaging, genomics, computation, machine learning and laboratory automation. We’re revolutionizing how our scientists can explore antibodies and the scale at which they can do so. This is life-changing research and you could be a part of 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. We’re a growing company with a high-throughput pipeline 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 visionaries and innovators. 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 help us get there.

**To apply**:
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