Rajdeep Das

Consultant

Chicago, Illinois, United States3 yrs 2 mos experience

Key Highlights

Strong academic background in Financial Mathematics.
Experience in quantitative research and AI chatbot development.
Awarded Manager Spotlight for innovative AR solutions.

Stackforce AI infers this person is a Fintech professional with strong quantitative research and data science capabilities.

Contact

Skills

Core Skills

Machine LearningAugmented Reality (ar)Data ScienceAlgorithm Design

Other Skills

AlgorithmsAutodesk InventorC++CommunicationData StructuresEngineeringEnglishFigma (Software)JavaLaTeXMATLABPandas (Software)Problem SolvingProgrammingPyTorch

About

I'm a graduate student in the Financial Mathematics program at the University of Chicago with an expected graduation in December 2025. I have a year of experience at Wells Fargo working in their technology innovations team and their AI chatbot team. I have done my bachelor's degree in Mathematics and Computing and I have continued to pursue my studies in the associated fields of probability theory and statistical modelling as it continues to pique my interest. I wish to pursue my career as a Quantitative Researcher or Quantitative Analyst and I'm keen on securing a full-time time in the same for 2025.

Experience

Daiwa securities capital markets

Quantitative Researcher - Univerity of Chicago Project Lab

Oct 2025 – Present · 5 mos · Chicago, Illinois, United States

Market Microstructure - Volume Forecasting and Jump Detection

Cme group

Quant Model Verification Intern - Clearing Risk

Jun 2025 – Present · 9 mos · Chicago, Illinois, United States · On-site

Flexible plan investments, ltd.

Quantitative Researcher - University of Chicago Project Lab

Jan 2025 – Mar 2025 · 2 mos

Researched well-cited academic publications for a good way to determine asset pricing returns based on the selection, latent extraction and transformation of 100 plus factors, into a concise set for explainability via linear combination of asset characteristics using instrument PCA and non-linear combination via an autoencoder model

Exponential technology inc.

Quantitative Researcher - University of Chicago Project Lab

Oct 2024 – Dec 2024 · 2 mos

Backtested strategies using zipline library. Developed new method of custom data ingestion and implemented trading calendars for custom data. Brainstormed trading strategies based on macroeconomic variables.

Wells fargo

2 roles

Software Engineer

Aug 2024 – Aug 2024 · 0 mo · Hyderabad, Telangana, India · On-site

Program Associate

Jul 2023 – Jul 2024 · 1 yr · Hyderabad, Telangana, India · On-site

LOB: Digital Technology and Innovations
Researched LLM Quantization and experimented on GCP for cost-benefit analysis
Used Computer vision and Natural Language Processing for multiple smaller use-cases
Worked on the design of an Augmented Reality Bill payment solution
Received Manager Spotlight Award

Python (Programming Language)Machine LearningFigma (Software)Augmented Reality (AR)

Indian institute of technology, delhi

Undergraduate Teaching Assistant

Feb 2023 – Jun 2023 · 4 mos

TA for MTL101: Linear Algebra and Differential Equations

Nokia

Data Science Intern

Aug 2022 – Oct 2022 · 2 mos

Studied on-field parameter and fault signal correlation under Nokia's Radio Frequency (RF) Optimization Project
Utilized immense raw source data files and applied Data Analysis to determine prime factors leading to faults being raised, automating a process previously done manually by engineers thereby reducing network downtime

Python (Programming Language)Pandas (Software)Data Science

Wells fargo

Technical Summer Intern

Jun 2022 – Jul 2022 · 1 mo

Developed online tool that supports spreadsheet functionalities and devised algorithm to convert excel macro code to be applicable on the same as necessary. Received Pre-Placement Offer for exceptional performance.

Algorithm DesignReact.js

Math-materate

ML Research intern

Dec 2021 – Jun 2022 · 6 mos

Improved enterprise's existing pipeline for handwritten image recognition to make it more suitable for mathematical symbols using PyTorch by skimming current research and incorporating their techniques
Achieved ~2% improvement in accuracy through experiments involving Gaussian blurring, de-skewing, de-slanting input images by extensively testing on both public datasets as well as on raw self-written images