Summary
✨ AI‑Generated
Join a global AI-focused team as a Senior Data Scientist and take end-to-end ownership of machine learning and generative AI systems that influence important product and business decisions. You will collaborate closely with product and business stakeholders and work alongside experienced data scientists and machine learning engineers. The role spans the complete ML lifecycle, including problem framing, data modeling, feature engineering, model training, deployment, serving, and production monitoring, with significant opportunities to apply Generative AI and LLMs.
Highlights
Own end-to-end machine learning and generative AI initiatives with strong impact across product and business decisions. Work closely with product and business leaders alongside a talented team of data scientists and machine learning engineers, while taking ownership across the full ML lifecycle from problem framing through production deployment and monitoring.
Description
Job DescriptionAs the leading delivery company in the region, we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders.
To realize our potential, we need to advance our platform to become much more intelligent in how it understands and serves our users.
As a Sr.
Data Scientist (AI & ML) on the global AI hub, your mission will be to design, build, and ship the machine learning and generative AI systems that power decisions across product and business.
You will own a particular domain end to end, working closely with product and business managers as part of a talented team of data scientists and machine learning engineers.
You will own the full ML lifecycle, from problem framing, data modeling, and feature engineering through model training, deployment, serving, and monitoring in production.
Many of our initiatives will focus on leveraging Generative AI and LLMs for tasks such as data enrichment, smart content understanding, and automated decision-making to enhance user experiences and business operations at scale.
Responsibilities
Framing ambiguous business problems as well-defined machine learning and data science problems, with clear, objective success criteria.
Providing high-quality, impactful insights and data-driven recommendations through rigorous analysis and automated reporting to drive strategic organizational choices.
Designing, building, and shipping end-to-end machine learning and generative AI systems in production — spanning data pipelines, feature engineering, model training, serving, and monitoring.
Taking on engineering-heavy work end to end: architecting robust ML-based systems, writing clean and scalable production code, and training, deploying, and maintaining reliable ML models that solve real business problems at scale.
Training, evaluating, and iterating on models — selecting the simplest, most appropriate algorithms and architectures to deliver measurable business value.
Leveraging LLMs and generative AI for data enrichment, smart content understanding, and automated decision-making within production systems.
Building and maintaining the data models, features, and pipelines that power model training and allow us to measure performance and its drivers for your area of focus.
Designing, planning, and analyzing experiments (A/B and multivariate tests) to rigorously measure model and product impact.
Developing deep familiarity with source data and its generating systems through documentation, collaboration with engineering teams, and systematic data profiling.
Partnering with product and business teams to identify high-impact opportunities and translate them into ML solutions and actionable, data-driven recommendations.
Mentoring other data scientists in their growth journeys.
Elevating engineering and ML best practices — improving our ways of working, tooling, MLOps, and internal training programs.
