Senior Data Scientist - MoneyLion
gen-digital is hiring a Senior Data Scientist focused on the MoneyLion vertical within the Product & Portfolio division in Kuala Lumpur. This role requires advanced analytical capabilities to solve complex business challenges related to financial product growth and user behavior modeling. Key responsibilities involve building predictive models (e.g., churn prediction, LTV forecasting), performing deep-dive statistical analysis, and translating findings into clear, actionable recommendations for product managers. The ideal candidate must possess a strong quantitative background, expertise in machine learning frameworks, and experience working with large transactional datasets. You will be critical in optimizing product features and enhancing overall customer engagement by leveraging the power of data science at scale. Remote work flexibility is offered for this key role.
(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)
Preparing
1800
Question set
250
Duration
Permanent
Focus areas
People Also Ask (FAQs)
What is the last date to apply for Senior Data Scientist - MoneyLion?
The closing date is listed in the official advertisement — apply as early as possible.
How do I apply for Senior Data Scientist - MoneyLion?
Use the official apply link on this page to submit your application. A step-by-step how-to-apply guide for this job is also linked below.
Is there negative marking in the gen-digital test?
The marking rule is announced per test in the advertisement and roll-number-slip instructions. Many MCQ screening tests have no negative marking, but always verify it for your specific test.
What is the passing marks for this test?
The official passing marks are generally 50%, but merit is competitive — scoring above 75% is a safer target to secure an interview call.
How can I prepare for the Senior Data Scientist - MoneyLion test?
Prepare with the free subject-wise MCQs, past papers and timed mock tests on PrepPro Academy — every question includes the answer with an explanation. Focus areas: Data Science, Machine Learning, Statistical Modeling, Python/R, Product Analytics.
Quick start
Review the job summary, then jump into prep with the same flow used for exam starts.