Senior Data Scientist (Mobility)
Grab
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See open jobs at Grab.See open jobs similar to "Senior Data Scientist (Mobility)" Sovereign’s Capital.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 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
Must-have
- 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.
Preferred to have:
- Experience with building recommender systems, customer segmentations, large scale forecasting, optimisation and etc
- Experience with algorithm and model development for large scale applications
- Hands-on experience with one or more big data processing frameworks such as Spark
- Experience with applying CI/CD principles to data science projects
- Experience with real-time data processing technologies such as Flink, Kafka, etc
- Experience with fine-tuning LLM and experience to build LLM-based use cases
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.
- Your well-being is our priority. Benefit from our holistic well-being initiatives through Wellbeing@Grab, including health programmes, informative webinars, and vibrant carnivals.
- Achieve a harmonious work-life balance with our FlexWork arrangements, allowing you to adapt and thrive in your personal and professional life.
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.
This job is no longer accepting applications
See open jobs at Grab.See open jobs similar to "Senior Data Scientist (Mobility)" Sovereign’s Capital.