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