Lead Data Scientist

Swish is building the next generation of food delivery by owning the entire value chain, from menu design and pricing to kitchen operations and customer experience. Backed by leading investors, we're building the future of food delivery by continuously launching new products, expanding into new markets, and creating innovative operating models.

About the role

We're looking for a hands-on Lead Data Scientist who can operate as both an individual contributor and a team leader. You'll own the data science roadmap, build models and infrastructure that drive real business impact, and mentor a growing team — all while working in a fast-paced, ambiguous, resource-constrained environment typical of an early/growth-stage startup.

What You'll Do

• Define and drive the data science strategy aligned with business and product goals
• Build, deploy, and monitor ML models in production (e.g., recommendation, forecasting, personalization, fraud detection — customize to Swish's use case)
• Design and run experimentation frameworks (A/B testing) to validate product and business decisions
• Partner closely with founders, product, and engineering to translate business problems into data solutions
• Build and mentor a small but high-performing data science team
• Establish best practices for data pipelines, model governance, and reproducibility where none may currently exist
• Communicate insights and recommendations clearly to non-technical stakeholders, including leadership

What We're Looking For

• 6–7 years of experience in data science/ML, with at least 1–2 years in a lead or mentoring capacity
• Strong hands-on experience with Python/SQL, and ML frameworks (scikit-learn, TensorFlow/PyTorch, etc.)
• Proven experience taking models from prototype to production
• Experience with A/B testing/experimentation design
• Comfort operating with ambiguity, incomplete data, and shifting priorities — startup experience strongly preferred
• Strong communication skills — able to influence decisions with non-technical stakeholders
• Bonus: experience building a data function from scratch (0→1)

Good to Have

• Experience applying demand forecasting, pricing, recommendations, customer segmentation, or optimisation at scale.
• Experience building and deploying end-to-end ML solutions in production.
• Strong exposure to ML infrastructure, MLOps, model monitoring, and experimentation frameworks.
• Experience working with cloud platforms such as AWS, GCP, or Azure.
• Familiarity with Spark, Airflow, Databricks, or similar data/ML tools.
• Experience designing A/B tests, experimentation frameworks, and product analytics.
• Experience mentoring data scientists and driving best practices in modelling, experimentation, and analytics.
• Ability to translate ambiguous business problems into scalable data science solutions and influence product/business decisions.

If “hands-on leader who loves a bit of startup chaos” made you nod, we should talk.