Senior Data Scientist

Company Description

About Grab and Our Workplace

Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.

Job Description

Get to know the Team:

• In the Mobility Data Science team you are at the core and center of Grab's ride hailing business. We are responsible for building and serving AI models to empower our pax' ride hailing experience in Grab.
• Our primary goals are to enhance our passenger experience, and drive decision-making that contributes to user growth and platform efficiency
• We leverage your mathematical modeling, statistical analysis, machine learning algorithms, and programming proficiency to drive data-driven decisions and help us conquer unique business challenges in the ride-hailing industry
  
  

Get To Know The Role

• Explore and extract insights from massive dataset of geospatial, behavioral, economic, and tens of millions of interactions of our passengers on our platform that can improve passenger experience and network efficiency
• Contribute to solving key business problems such as user experience, user growth, demand and supply balancing, and service differentiation.
• Develop and implement machine learning models to predict ride demand, passenger preferences, passenger cancellation/churn and improve the overall efficiency of the ride-hailing system.
• Continues improvement through iterative model enhancement and A/B testing
• Collaborate cross-functionally with software engineers, product managers, and operational teams to translate business needs into data solutions, implementing and deploying these solutions at scale.
• Contribute to team's innovation and IP creation
  
  

Qualifications

• Ph.D. graduate, or Master's Degree graduate with at least 3 years of experience, in Machine Learning, Statistics, Applied Mathematics, Computer Science, Economics, Operations Research, or a related field
• Extensive proven experience as a Data Scientist, preferably within the ride-hailing or related industries.
• Deep understanding of machine learning, deep learning, data mining, algorithmic foundations of optimization. Experience with machine learning framework (Tensorflow, pytorch, etc)
• Proficient in one or more of the following programming languages: Python, Scala.
• Self-motivated, independent learner, and willing to share knowledge with team members
• Excellent interpersonal and communication skills to foster collaboration with team members and effectively articulate insights to stakeholders.
• Ability to thrive in a dynamic, fast-paced work environment, managing multiple priorities simultaneously.
  
  

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 life's challenges.
• Balancing personal commitments and life's 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 provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.