Senior Data Scientist

2 days ago


North Region, Singapore GRABTAXI HOLDINGS PTE. LTD. Full time $120,000 - $200,000 per year

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

You'll be part of the SNP Data Science Personalization team, an established group dedicated to enhancing personalization experiences for Grab Food/Mart/Transport. We aim to develop cutting-edge Deep Learning models to deliver best user experiences. Our team comprises passionate Data Scientists with various expertise. If personalization and recommendation challenges excite you, we'd love to have you join us

Get to know the job

As a Senior Data Scientist, you will lead the solution and implementation of context-based Transport user understanding, including but not limited to feature engineering for Transport user profiles of price/promo/taxi type preferences/etc. in the current context (e.g. location/time/funnel). In addition, you will lead the solution and implementation of enhanced Transport personalization and Taxi Type ranking (include model training and model serving) for Grab's Mobility vertical with auto-adaptive capabilities. Your important work contributes significantly to Grab's growth.

You will be based onsite at Grab Singapore One North office and report to the Senior Data Science Manager

The Critical Tasks You Will Perform:

  • You will process large-scale datasets for model development and training.
  • You will design and implement efficient, scalable machine learning and deep learning algorithms.
  • You will evaluate how algorithms perform to improve relevance and personalization.
  • You will deploy models to production environments, focusing on robustness and maintainability.
  • You will run A/B tests to assess model performance, troubleshoot issues, and pinpoint opportunities for improvement.
  • You will keep up with recent advances in User Understanding, Recommendation ranking model, and LLMs.
  • You will work closely with backend engineers, product managers, analysts, and UX designers.

Qualifications

What Essential Skills You Will Need

  • You have a Bachelor's degree in Computer Science, Electrical/ Computer Engineering, or a related technical field.
  • You have at least 2 years of experience applying deep learning in domains like User Understanding, Recommendation Systems, or LLMs.
  • You can develop and deploy large-scale machine learning solutions.
  • You can translate requirements into technical solutions.
  • You have expertise in frameworks like TensorFlow or PyTorch.
  • You have software development abilities in Python

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.

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.


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