Data Scientist developing transactional fraud detection models for Desjardins’ banking and debit payments. Deploying analytics, dashboards, and mitigation strategies to protect members and clients.
About the role
- Senior Data Scientist working with Buffer to enhance data utilization for product and growth strategies. Collaborating across teams to drive insights and improve decision-making.
Responsibilities
- Serve as Buffer’s primary Data Scientist, supporting product and growth teams with analysis that informs decisions and prioritization
- Build and maintain behavioural and business models across acquisition, activation, engagement, retention, and monetization
- Lead complex analyses and research to identify product and growth opportunities, not just validate existing ideas
- Design, analyze, and interpret experiments across product and growth initiatives, including A/B tests and incrementality studies
- Partner with cross-functional teams to define success metrics, evaluation frameworks, and clear decision criteria
- Develop reusable datasets, models, and reporting patterns that reduce ad-hoc requests and increase self-serve capability
- Help evolve Buffer’s data systems, definitions, and measurement approach as we invest in analytics product features and personalization
- Use AI-assisted tools to streamline analysis, accelerate insight generation, and make data more accessible across the organization
- Communicate findings through clear narratives, documentation, and recommendations that influence decisions at multiple levels
- Improve the reliability of our analytics foundations through better modelling patterns, data quality checks, documentation, and clear sources of truth
- Partner with engineering to strengthen the data platform over time, including clearer ownership boundaries, better observability, and fewer fragile workflows
- Help shape our approach to AI-assisted analytics responsibly, including safer defaults, governance considerations, and a bias toward trusted semantic layers over free-form querying
Requirements
- Significant experience as a Data Scientist (or equivalent) in a SaaS or product-led growth (PLG) environment, with a strong understanding of SaaS metrics, growth loops, and monetization dynamics
- Deep hands-on experience with modern analytics stacks, including SQL-based data warehouses (BigQuery preferred; Snowflake or Redshift also relevant), BI tools such as Metabase, Mixpanel, Looker, or Mode, data transformation frameworks like dbt, and event tracking platforms like Segment
- Strong foundation in statistical analysis and causal reasoning, with fluency in SQL and advanced experience using Python or R for analysis, modelling, and data visualization
- Proven track record of owning complex, ambiguous analytical problems end-to-end, from framing and data exploration through to recommendations that influence product and business decisions
- Experience designing, analyzing, and interpreting experiments, including A/B testing, incrementality, and causal analyses, with a strong sense of methodological tradeoffs
- Experience building and maintaining analytical models across the full customer lifecycle, including acquisition, activation, engagement, retention, and monetization
- Demonstrated ability to balance high-impact strategic work with day-to-day analytical support, while systematically reducing ad hoc requests through better tooling, documentation, and self-serve systems
- Strong cross-functional partner to product managers, marketers, engineers, and designers, able to influence direction through data rather than operating as a service function
- Skilled at translating complex, messy data and ambiguous questions into clear metrics, narratives, and actionable insights for a wide range of stakeholders
- High degree of ownership and judgment, comfortable acting as the primary or most senior Data Scientist within a small team and shaping how data work gets done
- Experience leveraging modern data and AI-assisted tools to increase analytical leverage, improve insight accessibility, and scale impact across the organization
- Nice to have (strong plus): Experience in an analytics engineering style role (dbt-first modelling, metric layers, curated datasets, data quality testing)
- Familiarity with data orchestration and observability concepts (even if you have not owned them end-to-end)
- Experience working with privacy, governance, and access controls in analytical environments
- Practical experience using LLM tools for analysis in a way that is secure and auditable (for example: working only on approved datasets, avoiding sensitive fields, documenting assumptions)
Benefits
- Offers Equity
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