This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We're looking for an accomplished Data Science Manager to lead our data science team in delivering high-impact analytics and machine learning solutions across the business. You'll oversee a high performing team driving predictive modeling, customer insights, operational optimization, and product innovation using our rich datasets on Google Cloud Platform (GCP). Reporting directly to the VP of Data, you'll partner closely with product, engineering, and business leaders to translate data into actionable strategies. This role emphasizes strong people leadership, agile delivery, and the ability to communicate complex insights simply to non-technical executives. Experience with geospatial data is a plus but not required—we value broad data science expertise on modern cloud infrastructure. As an onX Data Science Manager, your essential job duties and responsibilities will look like: - Lead and mentor a team of 3 data scientists and ML engineers, focusing on technical guidance, career growth, and performance management. - Define and prioritize the data science roadmap, aligning projects with company goals across customer behavior modeling, forecasting, recommendation systems, and operational efficiency. - Oversee the design, development, and production deployment of advanced machine learning models and analytical solutions at scale. - Partner with stakeholders across product, engineering, and business teams to identify high-value opportunities and deliver measurable impact through data. - Promote best practices in data governance, model validation, and MLOps on GCP to ensure reliable, scalable solutions. - Drive agile ways of working: backlog refinement, sprint planning, and rapid iteration to accelerate business value. - Present insights and recommendations to executive leadership through clear storytelling, visualizations, and executive-friendly summaries. - Keep the team up to date on GCP innovations (e.g., Vertex AI, BigQuery ML) and emerging data science trends. Qualifications - 8+ years in data science, machine learning, or advanced analytics, with at least 3 years managing technical teams. - Proven track record of delivering business impact through data science in a product-driven tech environment. - Hands-on expertise with GCP data and ML services: BigQuery, Vertex AI, Dataflow, BigQuery ML, and related tools. - Strong proficiency in Python, Spark, SQL, and common data science libraries (pandas, scikit-learn, TensorFlow/PyTorch, etc.). - Demonstrated ability to explain technical concepts and results clearly to non-technical audiences. - Experience operating in agile environments with cross-functional collaboration. Requirements - Advanced degree (MS/PhD) in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field (preferred but not required). - Familiarity with Master Data Management principles to maintain high-quality, governed reference data (preferred but not required). - Exposure to geospatial tools (e.g., BigQuery GIS) or location-based analytics (preferred but not required). - Experience building and scaling cloud-native MLOps pipelines (preferred but not required). Benefits - Competitive salaries, annual bonuses, equity, and opportunities for growth. - Comprehensive health benefits including a no-monthly-cost medical plan. - Parental leave plan of 5 or 13 weeks fully paid. - 401k matching at 100% for the first 3% you save and 50% from 3-5%. - Company-wide outdoor adventures and amazing outdoor industry perks. - Annual “Get Out, Get Active” funds to fuel your active lifestyle in and outside of the gym. - Flexible time away package that includes PTO, STO, VTO, and Holidays.

• Partner closely with Product Managers to influence roadmaps using product insights • Be opinionated about what signals matter most at different stages of the product lifecycle • Define and evolve core product metrics related to adoption, engagement, and long-term value to support product decisions • Analyze product usage to identify opportunities to improve the developer experience • Frame success criteria and trade-offs to support product prioritization • Design analyses and experiments to inform product investments • Communicate insights clearly to senior stakeholders across Platform • Contribute to a culture of learning, collaboration, and thoughtful use of data

• Coaching a team of ~20 data specialists across three teams: Product Platform Data, Infrastructure Data, and Central Data Science • Partnering closely with Platform and Infrastructure Engineering leaders to shape and execute Canva’s Platform and Infrastructure data strategy • Responsible for overseeing the data science roadmap of core data infrastructure such as Canva's Experimentation Platform • Driving technical excellence and championing data-informed decision-making around platform performance, system reliability, and developer productivity • Leading analytics projects that influence how Canva builds scalable systems to serve billions of users • Enabling strategic investments in AI and advanced analytics to improve cost optimization, capacity planning, and system observability • Owning project prioritisation and capacity planning across three highly specialized and interconnected data teams

• Coaching a team of ~20 data specialists across three teams: Product Platform Data, Infrastructure Data, and Central Data Science • Partnering closely with Platform and Infrastructure Engineering leaders to shape and execute Canva’s Platform and Infrastructure data strategy • Responsible for overseeing the data science roadmap of core data infrastructure such as Canva's Experimentation Platform • Driving technical excellence and championing data-informed decision-making around platform performance, system reliability, and developer productivity • Leading analytics projects that influence how Canva builds scalable systems to serve billions of users • Enabling strategic investments in AI and advanced analytics to improve cost optimization, capacity planning, and system observability • Owning project prioritisation and capacity planning across three highly specialized and interconnected data teams