Suhas Shankar

Product Engineer

Amsterdam, North Holland, Netherlands2 yrs 1 mo experience

Most Likely To Switch

Key Highlights

Expert in quantitative finance and algorithms.
Experience with deep learning and time series forecasting.
Proven track record in software development and research.

Stackforce AI infers this person is a Quantitative Developer with expertise in Fintech and Research.

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Skills

Core Skills

Quantitative FinanceAlgorithmsMachine LearningSoftware Development

Other Skills

C (Programming Language)C++Computer ScienceDatabasesJavaObject-Oriented Programming (OOP)Problem SolvingProgrammingPython (Programming Language)Research SkillsSyntax

About

I am a Quant Developer in the commodities team at QRT, a leading hedge fund. Prior to this I was a Trader at Optiver - a market maker. Before this, I finished my undergrad in CSE at IIT Kanpur. In Spring 2022 I had the opportunity of working with Prateek Jain and Arun Suggala at Google, where we worked on mitigating some of the issues with multivariate time series forecasting methods. In Summer 2021, I interned at KAUST under Prof. Ying Sun where we worked on using deep learning techniques along with existing statistical methods for parameter estimation of geo-spatial data. In Summer 2020, I was involved with an open source gamedev organisation - Terasology, where I worked on their logging and metric monitoring system (Elastic Stack) as well as the path finding module for NPCs.

Experience

Qube research & technologies

Quantitative Developer

Jul 2024 – Present · 1 yr 8 mos · London Area, United Kingdom · On-site

Optiver

2 roles

Quantitative Trader

Nov 2023 – Apr 2024 · 5 mos · Amsterdam-Zuid, North Holland, Netherlands · On-site

Quantitative FinanceAlgorithmsSoftware DevelopmentDatabasesPython (Programming Language)Problem Solving

Trading Intern

May 2022 – Jul 2022 · 2 mos · Amsterdam, North Holland, Netherlands · On-site

Google

Research Intern

Jan 2022 – Apr 2022 · 3 mos · Bengaluru, Karnataka, India · On-site

Worked on problems related to time series forecasting
Incorporated a transformer style architecture to model explicit relationships between different time series in the multivariate setting

ProgrammingAlgorithmsSoftware DevelopmentMachine LearningComputer ScienceSyntax+5

Kaust (king abdullah university of science and technology)

Research Intern

May 2021 – Jun 2021 · 1 mo · Remote

Parameter estimation methods for stationary geospatial data exist
CNNs aid us in dividing non-stationary data into grids until we get stationary-like data
Perform parameter smoothing between all grids.
https://github.com/ecrc/GMC-DL/tree/Yiping/CNN_Stationary_Classification
Optimised the non-stationary matern kernel to achieve a speedup of 20%

ProgrammingAlgorithmsSoftware DevelopmentMachine LearningComputer ScienceSyntax+4

The terasology foundation

Student Developer (Terasology Summer of Code)

May 2020 – Jul 2020 · 2 mos · Remote

Worked on Pathfinding for AI Agents (https://suhas.postach.io/post/gallery)
Worked on a logging and metric monitoring solution using the ELK stack (https://suhas.postach.io/post/summary-post-of-logging-and-monitoring-in-terasology)

ProgrammingAlgorithmsSoftware DevelopmentComputer ScienceJavaProblem Solving