Shivansh Srivastava

CEO

Mumbai, Maharashtra, India6 yrs 9 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Expert in Interest Rate Derivatives Pricing models.
Strong background in Machine Learning and Data Science.
Proven track record in model management and enhancement.

Stackforce AI infers this person is a Fintech Quantitative Researcher with expertise in derivatives pricing and machine learning.

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Skills

Other Skills

Artificial Intelligence (AI)Python (Programming Language)

About

Experienced Quantitative Researcher working with Interest Rate Derivatives Pricing models. Skilled in Data Science, Computer Vision, Algorithms, Image Processing, and Deep Learning. Strong finance and Machine learning professional with a Master of Technology in Computational and Data Sciences focused towards Computer Vision from Indian Institute of Science (IISc).

Experience

Jpmorganchase

4 roles

Quantitative Research, Vice President - Rates Modelling

Promoted

Jan 2025 – Present · 1 yr 2 mos

Quantitative Research, Senior Associate - Rates Modelling

Promoted

Jan 2023 – Present · 3 yrs 2 mos

Management and enhancement of derivative pricing models, model reporting framework, end to end ownership of projects and direct interaction with Trading Desks, Model Review groups and other stakeholders.

Quantitative Research, Associate - Rates Modelling

Promoted

Jan 2022 – Jan 2023 · 1 yr

Enhancement, Review and Analytics of derivative pricing models for Linear and Exotic interest rate products.

Quantitative Research, Analyst - Rates Modelling

Jul 2019 – Jan 2022 · 2 yrs 6 mos

Stress testing, periodic reviews and trade validation of quantitative models used in pricing of interest rates derivatives.

Tesco

Data Science Research Intern

May 2018 – Jul 2018 · 2 mos · Bengaluru Area, India

Worked on making digital media discoverable, by using object level and event level scoring of
Marketing Images. The model was also supposed to be interpretable and modifiable enough to be understood and tweaked by content editors. The solution involved deep learning and used a Convolutional Neural Network based Object Detector followed by a bayesian model.