Get to Know the Team
The Fulfilment Tech Family is a foundational part of Grab enabling seamless coordination between our diverse marketplaces across Southeast Asia. We design real-time distributed systems and machine learning solutions to process hundreds of millions of requests per day driving efficient supply allocation pricing and order matching. Our mission:
- Deliver best-in-class products for our driver-partners.
- Maximise efficiency in fulfilling consumer demand rain or shine.
- Create sustainable efficient marketplaces that balance experience and cost for all stakeholders.
Were looking for a Senior Data Scientist to join our Fulfilment team and take the lead in Fulfilment strategy optimization bringing existing optimisation and automation of our pricing dispatch and supply management policies to the next level.
Get to Know the Role
Youll optimize cross-system fulfilment strategies and lead model development with user and driver behavioural prediction optimisation and reinforcement learning (RL) techniques. Your primary objective will be to enhance marketplace operations by constructing interpretable adaptable multi-agent RL systems or decision agents that can manage diverse objectives and disruptions.
Youll report to the Head of Data Science and work onsite at Grabs One North Singapore office.
The Critical Tasks You Will Perform
- Youll design and implement cross-system strategies to improve operational efficiency in fulfilling user demands and boosting driving utilisation.
- Youll build advanced DL and LLM models to capture the spatial-temporal patterns of dynamic marketplace conditions.
- Youll develop advanced ML/DL models to predict user and driver behaviours and use the behavioural insights to inform decision-making and platform interventions.
- Youll build and deploy interpretable adaptable optimisation models RL systems or decision agents that can handle multi-objectives and real-world disruptions.
- Youll collaborate with machine learning engineers and backend engineers to integrate the ML/DL optimization or RL models into real-time production systems.
- Youll create technical documents outlining the methodologies and findings of your work. Youll also present solutions to non-technical stakeholders.
Qualifications :
What Essential Skills You Will Need
- You hold a Master degree in Computer Science Operations Research Applied Mathematics or related field with at least 2 years of relevant experience.
- You have professional experience or academic publications in Reinforcement Learning Stochastic Control Behavioural Modelling Optimisation Under Uncertainty.
- You have experience developing and deploying ML models that incorporate online learning Markov Decision Processes or simulation-based optimization.
- You are fluent in Python and ML frameworks (e.g. PyTorch TensorFlow).
- You are familiar with distributed computing systems or scalable training platforms (e.g. Spark Ray).
- You can explore new ideas and learn new skills to accomplish tasks.
Additional Information :
Life at Grab
We care about your well-being at Grab here are some of the global benefits we offer:
- We have your back with Term Life Insurance and comprehensive Medical Insurance.
- With GrabFlex create a benefits package that suits your needs and aspirations.
- Celebrate moments that matter in life with loved ones through Parental and Birthday leave and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
- We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through lifes challenges.
- Balancing personal commitments and lifes demands are made easier with our FlexWork arrangements such as differentiated hours
What We Stand For at Grab
We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer we consider all candidates fairly and equally regardless of nationality ethnicity religion age gender identity sexual orientation family commitments physical and mental impairments or disabilities and other attributes that make them unique.
Remote Work :
No
Employment Type :
Full-time
Get to Know the TeamThe Fulfilment Tech Family is a foundational part of Grab enabling seamless coordination between our diverse marketplaces across Southeast Asia. We design real-time distributed systems and machine learning solutions to process hundreds of millions of requests per day driving effi...
Get to Know the Team
The Fulfilment Tech Family is a foundational part of Grab enabling seamless coordination between our diverse marketplaces across Southeast Asia. We design real-time distributed systems and machine learning solutions to process hundreds of millions of requests per day driving efficient supply allocation pricing and order matching. Our mission:
- Deliver best-in-class products for our driver-partners.
- Maximise efficiency in fulfilling consumer demand rain or shine.
- Create sustainable efficient marketplaces that balance experience and cost for all stakeholders.
Were looking for a Senior Data Scientist to join our Fulfilment team and take the lead in Fulfilment strategy optimization bringing existing optimisation and automation of our pricing dispatch and supply management policies to the next level.
Get to Know the Role
Youll optimize cross-system fulfilment strategies and lead model development with user and driver behavioural prediction optimisation and reinforcement learning (RL) techniques. Your primary objective will be to enhance marketplace operations by constructing interpretable adaptable multi-agent RL systems or decision agents that can manage diverse objectives and disruptions.
Youll report to the Head of Data Science and work onsite at Grabs One North Singapore office.
The Critical Tasks You Will Perform
- Youll design and implement cross-system strategies to improve operational efficiency in fulfilling user demands and boosting driving utilisation.
- Youll build advanced DL and LLM models to capture the spatial-temporal patterns of dynamic marketplace conditions.
- Youll develop advanced ML/DL models to predict user and driver behaviours and use the behavioural insights to inform decision-making and platform interventions.
- Youll build and deploy interpretable adaptable optimisation models RL systems or decision agents that can handle multi-objectives and real-world disruptions.
- Youll collaborate with machine learning engineers and backend engineers to integrate the ML/DL optimization or RL models into real-time production systems.
- Youll create technical documents outlining the methodologies and findings of your work. Youll also present solutions to non-technical stakeholders.
Qualifications :
What Essential Skills You Will Need
- You hold a Master degree in Computer Science Operations Research Applied Mathematics or related field with at least 2 years of relevant experience.
- You have professional experience or academic publications in Reinforcement Learning Stochastic Control Behavioural Modelling Optimisation Under Uncertainty.
- You have experience developing and deploying ML models that incorporate online learning Markov Decision Processes or simulation-based optimization.
- You are fluent in Python and ML frameworks (e.g. PyTorch TensorFlow).
- You are familiar with distributed computing systems or scalable training platforms (e.g. Spark Ray).
- You can explore new ideas and learn new skills to accomplish tasks.
Additional Information :
Life at Grab
We care about your well-being at Grab here are some of the global benefits we offer:
- We have your back with Term Life Insurance and comprehensive Medical Insurance.
- With GrabFlex create a benefits package that suits your needs and aspirations.
- Celebrate moments that matter in life with loved ones through Parental and Birthday leave and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
- We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through lifes challenges.
- Balancing personal commitments and lifes demands are made easier with our FlexWork arrangements such as differentiated hours
What We Stand For at Grab
We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer we consider all candidates fairly and equally regardless of nationality ethnicity religion age gender identity sexual orientation family commitments physical and mental impairments or disabilities and other attributes that make them unique.
Remote Work :
No
Employment Type :
Full-time
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