Principal Data Scientist

at  WestJet

Remote Work:
This role is classified as a hybrid position requiring collaboration at the Calgary head office (22 Aerial Place NE, Calgary) 3 days per week.
We are seeking a talented Principal Data Scientist to join the Advanced Analytics team. If you are passionate about data, have a proven track record of solving complex problems, coaching and mentoring other data scientists and want to contribute to meaningful projects, we encourage you to apply.
As a Principal Data Scientist, you will combine advanced technical skills and leadership capabilities with strategic insight to ensure data science efforts deliver measurable value. You will work collaboratively with other data scientists and cross-functional teams — including data engineers, product managers, and business stakeholders — to ideate, scope, design, develop and deploy advanced analytics solutions.

QUALIFICATIONS:

• Bachelor’s degree in Data Science, Mathematics, Statistics, Computer Science, or a related field with 7+ years of relevant experience.
• Deep knowledge of and proven expertise in applying statistical methods, machine learning algorithms, and data modeling techniques to business problems and a strong sense of when to adapt/combine approaches to suit the problem at hand.
• Strong proficiency in SQL and Python is a must have — familiarity with libraries and frameworks for data manipulation, machine learning, and data visualization is crucial. R and other similar languages would be an asset.
• Expertise in crafting analysis into compelling stories that convey findings from complex analysis to senior leadership, and business and technical stakeholders.
• Hands-on experience implementing MLOps and deploying models in production environments.
• Experience building dashboards in Power BI or Tableau.
• Proficiency in cloud platforms (e.g. Azure ML, Snowflake).
• Excellent problem-solving skills and the ability to work independently and collaboratively within a team.

• Technical Leadership: Lead technical strategy and execution for data science and AI initiatives, provide technical guidance and mentorship to less experienced team members. Provide technical leadership broadly across the organization on developing strategies for solving the organization’s biggest challenges with data and the implementation and deployment of ML and AI technologies.
• MLOps: Champion enterprise implementation and adoption of MLOps
• Data Analysis: Conduct exploratory data analysis to identify trends, patterns, and opportunities within large and complex datasets.
• Model Development: Develop and implement predictive and prescriptive models using machine learning and statistical techniques to solve business problems. Deploy and monitor models in production, ensuring their performance and scalability.
• Feature Engineering: Create and engineer features from various data sources to improve model performance.
• Data Storytelling: Articulate complex data insights to business leaders and stakeholders in accessible terms using storytelling and data visualization techniques and tools (Power BI and Tableau).
• Community & Research: Keep current on data science and AI tools, techniques and best practices and provide advice to citizen data analysts/scientists and the community of practice.