Lead Data Scientist (Supply Planning)

Company Description

About Grab and our workplace

Grab is Southeast Asia's leading superapp. We are dedicated to improving the lives of millions of users across the region by providing them everyday services such as deliveries, mobility, financial services, enterprise services and others. More than that, we provide the opportunity for them to have a better life. And that aspiration starts inside Grab because we believe in a seamless blend of work and home life, making every aspect of life better for all.

Guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles—the 4Hs: Heart, Hunger, Honour and Humility—we work to create economic empowerment for the people of Southeast Asia. With our unwavering commitment to our values, we believe that we're more than a service provider; we're agents of positive change.

Job Description

Get to know the Fulfilment Team

The Fulfilment tech family is one of the pillars enabling Grab to out-serve our consumers and partners in different businesses and marketplaces across Southeast Asia. We are working on high throughput, real-time distributed systems that use sophisticated machine learning techniques to solve hundreds of millions of requests per day. Our mission is to offer the best-in-class products and experiences to our driver partners as to increase adoption and engagement of our services. Improve driver partner opportunities and efficiency in order to fulfill customer orders without fail, rain or shine. And to create efficient marketplaces by determining an optimal price that is both sustainable and loved by our partners and consumers.

Get to know the role

We are currently seeking a Senior Data Scientist to augment our team. In this role, you will have the unique opportunity to leverage big data to derive actionable insights and devise solutions. You will be tasked with developing deep learning, reinforcement learning, and optimisation models to address supply repositioning, activation, and incentivisation challenges within Grab's marketplace. Furthermore, you will be responsible for deploying these models at scale.

The Day-to-Day Activities

• Collaborate with stakeholders to identify the business problems and requirements about driver repositioning, activation and incentivisation
• Translate the business problems into data science problems, formulate them and design the potential solutions.
• Identify the relevant data, retrieve them for the explorative analysis, feasibility study and solution prototyping
• Create technical documents outlining the methodologies and findings, and present solutions to business stakeholders clearly and precisely to achieve alignment
• Discuss and design the overall architectures for the solutions with different technical team, develop the DS solutions and support their integration with the corresponding Engineering systems
• Participate in the code and design review to maintain high development standards, build the dashboard to keep monitoring the health of DS solutions, and continuously maintain and improve the solutions.
• Design and construct experiments to evaluate the impacts of solutions.
• Mentor junior team members

Qualifications

The Must-Haves

• Master or PhD Degree in computer science, operations research, mathematics, statistics or related fields
• 3 + years of experience in one or more of the following areas: reinforcement learning, deep learning, optimisation, operations research
• Experience in developing production-grade ML systems including exploratory analysis, feature engineering, developing data pipelines, observability and maintenance etc
• Strong aptitude in statistics and large-scale data analytics
• Proficiency in Python and SQL
• Experience with one of ML frameworks, such as TensorFlow or PyTorch
• Experience with distributed systems and cloud services (AWS, GCP, AZURE)
• Good written and oral communication skills. Strong teamwork and interpersonal skills
• Self-motivated and independent learner, detail-oriented and efficient time manager in a dynamic and fast-paced working environment

The Nice-to-Haves

• Experience in customer profiling
• Experience in developing recommendation systems
• Academic research experience in the field of operations research or reinforcement learning
• Experience applying reinforcement learning for solving real-world problems (robotics, finances, etc.)
• Contributions to open source projects

Additional Information

Benefits at Grab:

We care deeply about your well-being and are committed to supporting you every step of the way. Here are some of the global benefits we offer:

• Protect and provide for your loved ones with peace of mind, knowing we have your back with Term Life Insurance and comprehensive Medical Insurance.
• Craft a benefits package that suits your unique needs and aspirations with GrabFlex, because we believe in empowering you to thrive.
• Embrace the magic of new life and create lasting memories with your family through Maternity and Paternity Leave.
• Life can be overwhelming, but you're never alone. Our confidential Grabber Assistance Programme is here to guide and uplift you and your loved ones through life's challenges.

We've got many different benefits hyper localised in each country. Speak to your recruiter during your interview to find out more.

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. If you require accommodations to fully participate in the recruitment process, you are encouraged to include your request(s) when applying.

We deliver the greatest impact and ideas when we bring together diverse perspectives. It is what enables us to spread opportunities to Grabbers and our partners. It's not a box-ticking exercise; it's who we are.