GoTo Group - Lead Data Scientist
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Requirements
• Translate complex business challenges into well-defined technical problems solvable with data, statistics, and machine learning. • Collaborate with product managers, engineers, and business stakeholders to build and scale data science solutions that power pricing across ride-hailing, food delivery and logistics • Own the full ML lifecycle—from ideation and research to model development, pipeline implementation, deployment, experimentation, and driving measurable business outcomes. • Improve the efficiency of our dynamic pricing system using techniques from econometrics, causal inference and simulation • Design and interpret experiments to measure model impact, working with analysts and product teams to ensure rigorous evaluation and clear success metrics. • Monitor and evaluate model performance, identifying areas for improvement and proposing solutions • Communicate insights, trade-offs, and technical decisions effectively to cross-functional stakeholders, and operate with a high degree of autonomy in ambiguous problem spaces. • Bachelor’s or Master’s degree in Computer Science, Statistics, Machine Learning, or a related quantitative field, with 5-8 years of relevant experience • Experience working on the core dynamic pricing engine for a ride-hailing company or other similarly dynamic domain • Solid understanding of statistics and machine learning fundamentals, with projects demonstrating practical application. • Proficiency in Python and SQL, and familiarity with data analysis or modeling libraries. • Strong analytical thinking and problem-solving skills, with the ability to reason from data and communicate findings clearly. • A willingness to learn fast, take initiative, and work collaboratively in a cross-functional team. • Curiosity, humility, and a drive to apply data science to real-world problems at scale. • Exposure to tools like Jupyter, Git, or Docker, and familiarity with software development workflows. • Experience working with real-time ML systems—even at a project or academic level. • Familiarity with cloud platforms (e.g. GCP, AWS, AliCloud), or modern data stack tools. • Contributions to open-source projects or other public showcases of your work.
Responsibilities
• Translate complex business challenges into well-defined technical problems solvable with data, statistics, and machine learning. • Collaborate with product managers, engineers, and business stakeholders to build and scale data science solutions that power pricing across ride-hailing, food delivery and logistics • Own the full ML lifecycle—from ideation and research to model development, pipeline implementation, deployment, experimentation, and driving measurable business outcomes. • Improve the efficiency of our dynamic pricing system using techniques from econometrics, causal inference and simulation • Design and interpret experiments to measure model impact, working with analysts and product teams to ensure rigorous evaluation and clear success metrics. • Monitor and evaluate model performance, identifying areas for improvement and proposing solutions • Communicate insights, trade-offs, and technical decisions effectively to cross-functional stakeholders, and operate with a high degree of autonomy in ambiguous problem spaces. • Bachelor’s or Master’s degree in Computer Science, Statistics, Machine Learning, or a related quantitative field, with 5-8 years of relevant experience • Experience working on the core dynamic pricing engine for a ride-hailing company or other similarly dynamic domain • Solid understanding of statistics and machine learning fundamentals, with projects demonstrating practical application. • Proficiency in Python and SQL, and familiarity with data analysis or modeling libraries. • Strong analytical thinking and problem-solving skills, with the ability to reason from data and communicate findings clearly. • A willingness to learn fast, take initiative, and work collaboratively in a cross-functional team. • Curiosity, humility, and a drive to apply data science to real-world problems at scale.
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