About Symcor
Symcor enables secure data exchanges and supporting business processes, to help clients succeed in an evolving digital world. Trusted by Canada’s largest institutions for over 25 years to support their digital transformations, Symcor aligns industry participants to solve common challenges in the most effective and efficient way. Our goal is for every employee to feel valued and accepted as part of the team and connected to Symcor and our values, fostering a sense of belonging. Symcor is an equal opportunity employer and is committed to providing an accessible recruitment process. If you need accommodation for the interview process, just let us know!

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For more information, visit www.symcor.ca
The Opportunity
The Machine Learning Lead will play a pivotal role in shaping and executing our Machine Learning/Artificial Intelligence (ML/AI) strategy at Symcor. This role demands a seasoned professional with a deep understanding of machine learning algorithms, strong leadership skills, and a proven track record of delivering successful machine learning projects. Will collaborate cross-functionally with product and teams to integrate machine learning solutions into our products and services across multiple lines of service.
This is a hybrid role with no mandatory days in office. We do expect that the candidate is available to come in for purpose-driven planned, and ad-hoc meetings such as in-person team collaborations, team events, workshops, etc.

EDUCATION:

• Bachelor university degree in a related discipline (e.g., statistics, applied mathematics).

EXPERIENCE:

• 6-9 Years.
• 7+ years of experience in data science.
• 2+ years of leading teams as ML Lead.
• A portfolio of completed projects through one full life cycle, from concept to full production.

SKILLS:

• Machine learning frameworks: Tensor Flow, XG Boost, Scikit-learn.
• Data analysis tools: NumPy, Pandas.
• Expertise with both relational and non-relational database.
• Proficiency with data mining, mathematics, and statistical analysis.
• Advanced experience in pattern recognition and predictive modeling.
• Programming Languages: Python, R, SQL.
• Data Visualization: Power BI, Qlik, Tableau or equivalent.
• ETL Tools: Databricks, Snowflake, Azure Data Factory, Azure Data Lake Storage.
• Cloud platforms: Azure, AWS, etc.
• Excellent oral and written communication skills.
• Excellent presentation skills.
• Ability to work effectively in a dynamic, research-oriented group that has several concurrent projects.
• Knowledge of operating systems (Microsoft, Unix, Linux).
• Project Management Methodologies: Agile (Scrum).
  What’s In It For You

At Symcor, we define our success by what we help others achieve. We were created to support our clients and, through our products, services, and solutions, protect and strengthen their brands. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

• A comprehensive Total Rewards Program that includes a competitive compensation package, flexible benefits and time away options, retirement and savings plans and a commitment to your overall health and well-being through our myWell-being program.
• Leaders who support your development through coaching and managing opportunities.
• Ability to make a difference and lasting impact.
• Opportunities to do challenging work and progressively take on greater accountabilities for growth.
• We foster an inclusive atmosphere of One Symcor with our philosophy of +1 Unique You®, we harness each employee’s uniqueness, different skills, backgrounds, and perspectives to contribute to mutual success. It is the diversity of our people and the inclusive environment that has been and will always be key to Symcor’s success.

Symcor is an equal opportunity employer and is committed to providing an accessible recruitment process. Upon request, we will provide accommodation for candidates

• Assess the current state of Machine Learning and AI practices and establish a roadmap to expand the ML/AI use at Symcor.
• Responsible for deploying machine learning models into development and test environments, as well as assisting with production deployments and automation, while ensuring scalability, reliability, and performance.
• Execute analytical experiments and implement analytical models in production.
• Monitor model performance and health in production.
• Clean, validate and maintain data accuracy, uniformity and consistency.