Staff Data Scientist

We’re looking for a Staff Data Scientist to shape the future of our marketplace through advanced analytics, experimentation, and machine learning. You’ll define the technical direction of data science initiatives, influence product strategy, and mentor other data scientists to elevate our analytical standards and impact.

As a Senior Technical Leader, you’ll partner closely with Product, Engineering, and Operations teams to turn data into decisions that improve reliability, growth, and user experience across markets.

- Lead data science initiatives with company-wide impact — from experimentation frameworks to ML systems and causal inference methods

- Define best practices and standards for experimentation, modelling, and analytical excellence across teams

- Partner with Product and Engineering leadership to identify and prioritise high-leverage opportunities

- Develop and deploy scalable models and data products that drive measurable business outcomes

- Mentor and coach other data scientists and analysts, helping them deliver higher-impact work

- Communicate insights and recommendations to senior leadership, influencing product and growth strategy

- Contribute to Heetch’s data platform evolution, ensuring data quality, reliability, and efficiency at scale

- Have a deep understanding of statistics, experimentation, and causal inference, and know how to apply them pragmatically

- Are fluent in Python, SQL, and modern data/ML tooling

- Have experience bringing ML models into production and measuring their long-term business impact

- Are skilled at translating complex technical findings into actionable business insights

- Are a collaborative leader who mentors peers and sets high standards for analytical rigor and impact

- Have 7+ years of experience in data science, ideally with 2–3 years in a Staff-level or tech-lead capacity

- Experience in marketplace or pricing systems (supply–demand modelling, matching, or fraud detection)

- Experience leading data science guilds, chapters, or communities of practice

- Familiarity with MLOps, feature stores, or causal inference frameworks