Senior Data Scientist (Integrity)
Bengaluru, Karnataka, India
Grab
Grab is Southeast Asia’s leading superapp. It provides everyday services like Deliveries, Mobility, Financial Services, and More.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 Our Team
The Grab Integrity team is dedicated to protecting the Grab platform from multiple types of fraud and safety incidents. Our team leverages rich datasets ranging from payment risk prediction using sequence-based models to detecting money laundering with graph algorithms and ensuring platform safety. We research new methods to stay ahead of emerging fraud tactics, contributing to the creation of thoughtful and secure products.
Get to Know the Role:
Reporting to the Head of Data Science, FinTrust and FinID, this will be a Full-time role in Bangalore
The Critical Tasks You Will Perform:
- Collaborate with stakeholders to understand and convert operational issues into data science problems, aligning efforts with our strategic goals.
- Stay updated with the latest research and advancements in the field, incorporating the latest models and techniques to address new fraud tactics.
- Handle data preparation and augmentation, using multiple data types to create comprehensive datasets for model training.
- Train and improve machine learning models, ensuring accuracy and efficiency in fraud detection through careful selection and tuning of algorithms. Types of algorithms the team work on include Graph Neural Networks, Transformer/ Sequence models, finetuned LLMs, Boosted Trees
- Deploy models into production, managing their performance, and working with data scientists, software engineers, and product managers to ensure seamless integration and ongoing improvements.
Qualifications
The Essential Skills You Will Need:
- Degree in computer science, physics, statistics, or a related quantitative field.
- Proficiency in Python, SQL, and programming skills, with familiarity with numeric libraries, containers, and modular software design
- 4 years+ experience of standard machine learning libraries such as TensorFlow, PyTorch, XGBoost, LightGBM, and Scikit-learn
- Understanding and some experience using traditional ML techniques like Boosted Trees, and deep neural network architectures, like CNNs, RNNs, Transformers.
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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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Architecture Computer Science LightGBM LLMs Machine Learning ML models Model training Physics Python PyTorch Research Scikit-learn SQL Statistics TensorFlow Transformers XGBoost
Perks/benefits: Career development Medical leave Parental leave
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