Data Engineer for Federated Learning

Priority may be given to the following designated employment equity groups: women, Indigenous peoples* (First Nations, Inuit and Métis), persons with disabilities and racialized persons*.

 

* The Employment Equity Act, which is under review, uses the terminology Aboriginal peoples and visible minorities.

 

Candidates are asked to self-declare when applying to this hiring process.

 

City: Saskatoon 

Organizational Unit: Aquatic and Crop Resource Development 

Classification: RO 

Tenure: Term 

Duration: 2 years 

Language Requirements: English 

 

Work Arrangements: 

• Due to the nature of the work and operational requirements, this position will require full-time physical presence at the at the NRC work location identified.

 

The Role

The Canadian agricultural sector faces challenges to increase productivity, efficiency, and sustainability with the same or fewer resources. Designing transformative AI and machine learning systems for wide-spread industry adoption and boosting productivity is critical to our agriculture industry. From a crop improvement perspective, developing machine and deep learning algorithms incorporating large, distributed datasets presents a unique opportunity to increase model efficacy and ultimately the genetic gains and adaptability of Canada’s field crops. At the NRC’s Aquatic and Crop Resource Development (ACRD) research centre, we are investing in technologies to create AI sandboxes for informative machine learning and generative AI solutions and expanding our digital capabilities to develop innovative tools including knowledge graph databases for crop improvement and agricultural productivity. We invite you to join our team to take crop omics data and federated learning to the next level and be an integral member delivering industry-leading solutions. Interacting with colleagues across ACRD and collaborating nationally and internationally, the successful candidate would be someone who shares our core values of Integrity, Excellence, Respect and Creativity.

 

As a Research Associate (RA), the successful candidate will play an essential role contributing to data standardization and interoperability for data integration as well as contributing to design of a federated learning architecture. Responsibilities include the construction and maintenance of a data integration pipeline encompassing different crop data domains relevant to the Canadian Agricultural industry. The RA will ensure that solutions support federated network approaches. The successful candidate will integrate in-house multi-omics and relevant public datasets by implementing and harmonizing metadata schemes, and other data management practices. The RA will also participate in a team effort to create a self-service analytics platform, as well as developing and improving APIs and web interfaces. The architectures will support AI model generation and data analysis through federated learning approaches which are integral to creating a modern data platform for boosting Canada’s agriculture sector.

Screening Criteria

Applicants must demonstrate within the content of their application that they meet the following screening criteria in order to be given further consideration as candidates:

Education

PhD degree in Computer Science, Engineering, or related field. 

 

Preference will be given to candidates with relevant post-doctoral or industry experience. 

 

For information on certificates and diplomas issued abroad, please see Degree equivalency

Experience

1. Experience and proficiency in programming languages such as Python.
2. Experience in using neural networks and ML libraries such as TensorFlow or PyTorch.
3. Experience in building and querying relational to non-relational databases including graph and document-oriented databases such as NeO4J or MongoDB.
4. Experience with data management strategies, data standards, and semantic data.
5. Experience collaborating with cross-functional teams to identify key challenges and translate them into workable production pipelines.
6. Experience implementing federated learning on multiple local datasets or distributed datasets would be an asset.
7. Experience with biological datasets, bioinformatics, and/or ontologies would be an asset.

Condition of Employment

 Reliability Status

Language Requirements

English 

 

Information on language requirements and self-assessment tests

Assessment Criteria

Candidates will be assessed on the basis of the following criteria:

Technical Competencies

1. Ability to produce efficient Python code.
2. Ability to apply deep learning algorithms.
3. Knowledge of Neo4j, MySQL, MongoDB and query languages (such as Cypher, SQL), or equivalent solutions.
4. Knowledge of container and cloud storage solutions.
5. Knowledge of the FAIR principles (Findable, Accessible, Interoperable, and Reusable) and methods for making data FAIR such as OBO (or OWL) ontologies.
6. Knowledge of controlling for data confounders which impact model performance.
7. Practical knowledge of open-source frameworks for federated learning will be considered an asset.

Behavioural Competencies

• Research – Communication (Level 2)
• Research – Initiative (Level 2)
• Research – Results orientation (Level 3)
• Research – Self-knowing and self-development (Level 2)
• Research – Teamwork (Level 2)

Competency Profile(s)

For this position, the NRC will evaluate candidates using the following competency profile(s): Research

 

View all competency profiles

Compensation

The intent of this hiring action is to staff this position at the AsRO level, which is an early-career level position with a salary range of $82,338 to $114,354. 

 

NOTE: Please note that the full RO/RCO salary scale has five levels. Salary determination will be based on a review of the candidate’s expertise, outcomes and impacts of their previous work experience relative to the requirements of the level.  As such, the initial salary could be within another level of the RO/RCO salary scale (i.e. above or below the intended level for this position).

 

NRC employees enjoy a wide-range of competitive benefits including a robust pension plan, comprehensive health and dental coverage, disability and life insurance, office closure at the end of December, and additional supports to enhance your well-being throughout your career and beyond.

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Discover the possible

Anything is possible at the National Research Council (NRC). As Canada’s leading research and technology organization, our world-renowned research pushes the boundaries of science and engineering to make the impossible, possible. Every day we explore new ideas through innovative research and help companies discover possibilities that impact Canada’s future and the world.

 

At the NRC, you’ll also discover new possibilities. Our supportive workplace fosters a culture of creativity, welcoming fresh perspectives and innovation at all levels. We value teamwork. You’ll collaborate across multiple fields and with the brightest minds to find creative solutions. Most importantly, you’ll discover what’s possible within you as you grow, make valuable contributions and progress in your professional journey. From ground-breaking discoveries to a life-changing career, discover your possible at the NRC.

Notes

• Relocation assistance will be determined in accordance with the NRC’s directives.
• ​A pre-qualified list may be established for similar positions for a one year period.
• Preference will be given to Canadian Citizens and Permanent Residents of Canada. Please include citizenship information in your application.
• The incumbent must adhere to safe workplace practices at all times.
• We thank all those who apply, however only those selected for further consideration will be contacted.

 

 

Please direct your questions, with the requisition number (23422) to:

E-mail: NRC.NRCHiring-EmbaucheCNRC.CNRC@nrc-cnrc.gc.ca

Telephone: 343-990-7339 

Closing Date: 20 January 2025 – 23:59 Eastern Time

 

 

For more information on career tools and other resources, check out Career tools and resources

 

*If you are currently a term or continuing employee at NRC, please apply through the SuccessFactors Careers module from your NRC computer.