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

You will join a collaborative and forward-thinking team focused on harnessing the power of data to transform workflows, optimize efficiency, and guide strategic decisions. Our team engages with cross-functional stakeholders to turn data insights into impactful business outcomes.

Get to Know the Role

As a Data Scientist (Analytics), you'll report into Analytics Manager II and work onsite at Grab One North Singapore office. Your role will be pivotal in designing and implementing data-driven solutions by leveraging advanced analytics and machine learning techniques. You will extract actionable insights from complex datasets and ensure these insights are communicated to stakeholders and integrated into business strategies.

The Critical Tasks You Will Perform

You will:

• Develop and deploy machine learning models to help resolve organisational challenges and enhance operational efficiency.
• Take ownership of analysing large-scale datasets to identify trends, patterns, and actionable insights for decision-making.
• Collaborate with cross-functional teams to translate business requirements into practical analytical solutions.
• Design and execute statistical analyses and experiments (such as A/B tests), presenting conclusions to evaluate initiatives.
• Create dashboards and visualisations to communicate findings to non-technical stakeholders across different functions.
• Lead efforts to optimize existing machine learning models and introduce innovative algorithms.
• Propose and establish best practices for statistical analysis and machine learning. Stay informed about industry advancements in data science techniques and apply these to your work.

Qualifications

What Essential Skills You Will Need

You have:

• Bachelor's degree in Computer Science, Software Engineering, Mathematics, Physics, or a related field with at least 1 year of experience in data science or machine learning roles
• Expertise in Python and machine learning frameworks such as TensorFlow or PyTorch for developing and optimising models.
• Understanding of advanced analytical methods and causal inference to design and conduct impactful experiments.
• Hands-on experience with tools like Spark to process and analyze large-scale datasets.
• Solid command of SQL for querying and working with relational databases.
• Use visualisation tools to craft dashboards and communicate insights to stakeholders.
• Analytical methods to troubleshoot model performance and draw meaningful conclusions.
• Experience working in cross-functional environments, with the ability to align analytical solutions with business needs.

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 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.