Data Scientist jobs in Canada

Employer Industry: Financial Technology

Why consider this job opportunity:
- Opportunity for career advancement and growth within the organization
- Engage in impactful work that enhances global economic accessibility
- Collaborative and innovative work environment
- Mentor junior engineers and foster a culture of best practices
- Work with cutting-edge technologies like Spark, Scala, and Airflow
- Contribute to the development of data solutions that drive business success

What to Expect (Job Responsibilities):
- Lead the design, development, and maintenance of data pipelines, models, and applications supporting Growth, Sales, and Marketing functions
- Develop subject matter expertise in managed systems and set SLAs for data pipelines and datasets
- Enhance data foundations, including infrastructure and tools, for collaboration across teams
- Design and implement frameworks for seamless data integration between internal systems and third-party sources
- Utilize analytical and problem-solving skills to address complex issues and communicate findings to stakeholders

What is Required (Qualifications):
- 10+ years of hands-on software development experience, with a focus on data-intensive applications
- Proven experience leading technical initiatives and influencing cross-functional teams
- Strong background in writing and debugging data pipelines using distributed frameworks such as Spark or Hadoop
- Proficient in at least one backend development language (e.g., Scala, Java, or Go) and solid SQL expertise
- Exceptional analytical skills with an in-depth understanding of data quality issues

How to Stand Out (Preferred Qualifications):
- Familiarity with Growth or GTM roles and their specific data needs
- Experience with large-scale data systems like Presto/Trino, MongoDB, and Apache Pinot
- Experience in building, deploying, and optimizing machine learning models
- Understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
- Knowledge of integrating machine learning models into data pipelines for enhanced insights

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