Data Science Manager
vor 2 Wochen
We are on the lookout for a ( Senior) Manager, Data Science (all genders) to lead rider-centric modelling in Logistics. Data Science is integral to our operations, powering everything from acquisition through retention, demand forecasting to staffing allocation, route estimation to planning, payments to fraud prevention, and everything in between. In this role, you will drive initiatives towards supply/demand equilibrium, enhancing fleet performance, seamless experiences, and optimizing costs throughout our global footprint.
Lead a team of data scientists to develop and implement algorithms to optimize rider logistics operations: demand forecasting and staffing, state-of-the-art behavioural models for lifetime value prediction, session preferences, churn or fraud risk, delivery acceptance, routes, and payments.
Establish a long-term vision, strategy, and the roadmap for its execution to maintain a competitive edge and be prepared for unforeseen challenges.
Collaborate closely with cross-functional teams including product, engineering, and operations to identify key business challenges, prioritize and develop data-driven solutions that improve the rider experience and operational efficiency.
Own the Data Science function (practices, processes, standards, and culture) for Rider logistics that can move and iterate fast with the evolving business landscape.
Grow and scale teams that produce high-quality output in a sustainable manner, enable personal growth and learning, and foster a positive work environment.
Stay up-to-date with the latest developments in data science and machine learning, exploring new techniques and technologies to enhance the effectiveness and scalability of our algorithms and models.
Qualifications
5+ years of experience building data science products, including 2+ years in a managerial or leadership role.Strong expertise in machine learning, statistical analysis, and ensuring high-quality methodological choices and model validation practices.
Demonstrated experience delivering production models, ideally in several of the following areas: Time Series, Bayesian Models, Reinforcement Learning, Operations and Optimization, or Fraud.
Experience working with petabyte-scale data for feature engineering and machine learning models.
Excellent proficiency in Python and SQL, familiarity with cloud platforms such as AWS or GCP, and MLOps tools such as metaflow or kubeflow.
Excellent communication and presentation skills, with the ability to translate statistical findings into operational insights, and work effectively across multiple teams or stakeholders.
Experience in complex experiment design that can isolate the impact of changes introduced in our systems.
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