Lead the design, execution, and interpretation of A/B tests and other experiments to evaluate product changes; apply causal inference methods to understand the “why” behind results.
Work closely with Product Managers and leaders to identify growth levers, assess trade-offs, and influence investment decisions.
Apply statistical modelling, segmentation, clustering, and predictive techniques to uncover deeper insights into user behaviour and product performance.
Translate complex analysis into clear narratives that drive decisions across technical and non-technical audiences.
Share knowledge, coach junior team members, and foster a culture of curiosity and continuous learning.
Partner with Product Managers, Engineers, and other stakeholders to ensure the data foundation is reliable and fit for analysis.
Act as a strategic thought partner, bringing rigour to measuring product impact and surfacing opportunities.
Requirements
5+ years of experience in product-focused data science or analytics, ideally in SaaS or eCommerce.
Strong background in experimental design, causal inference, and statistical analysis.
Proficiency in SQL and a programming language such as Python or R.
Comfort applying predictive models and clustering techniques; and strong judgment on when descriptive and causal approaches are best.
Excellent communication and storytelling skills; ability to translate data into compelling narratives that influence leaders.
Proven ability to partner with product managers and analytics engineers to shape strategy, not just report on it.
A mix of curiosity and humility: eager to learn, and just as eager to share.
Bonus: Industry experience in health tech, integrative medicine, or related fields.
Bonus: Experience with BI tools like Looker or Tableau, and familiarity with cloud platforms such as Snowflake or AWS.
Bonus: Knowledge of deep learning, natural language processing, or other advanced machine learning methodologies.