Quantitative Researcher
Hyperbolic invites applications for a Quantitative Researcher role within our dynamic Finance department. This position is central to developing, backtesting, and deploying quantitative strategies designed to extract alpha from complex market data sources. You will be responsible for the entire lifecycle of quantitative models—from initial hypothesis generation and rigorous statistical validation to implementation in high-performance trading systems. Key activities include advanced econometric modeling, machine learning application to financial time series, and comprehensive risk assessment. The ideal candidate must possess a strong academic background (e.g., PhD/Masters) in quantitative fields, coupled with expertise in languages like Python or R, deep knowledge of stochastic processes, and an acute understanding of capital markets dynamics. This is a highly analytical role for finance professionals seeking cutting-edge challenges.
(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)
Preparing
1500
Question set
280
Duration
Permanent
Focus areas
People Also Ask (FAQs)
What is the last date to apply for Quantitative Researcher?
The closing date is listed in the official advertisement — apply as early as possible.
How do I apply for Quantitative Researcher?
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 hyperbolic 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 Quantitative Researcher 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: Quantitative Analysis, Financial Modeling, Statistics, Python/R, Time Series Analysis.
Quick start
Review the job summary, then jump into prep with the same flow used for exam starts.