Staff Data Scientist

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