About The Role

About the Role

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.

What You’ll Do

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

You’ll Thrive In This Role If You

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

Nice to Have

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