Bisesh Padhi

Business Analyst

Berlin, Berlin, Germany1 yr 1 mo experience

Key Highlights

Expert in quantitative analysis and financial modeling.
Strong background in digital design and backend development.
Proven track record in developing profitable trading models.

Stackforce AI infers this person is a Fintech and Semiconductor professional with strong backend development skills.

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Skills

Core Skills

Quantitative Analysis (finance)Data AnalysisDigital DesignBackend Development

Other Skills

Artificial Neural NetworksBashBranch PredictionCCommunicationComputer ArchitectureDerivatives TradingDigital ElectronicsFutures and Options (F&O)JUnitJavaLinuxMATLABMachine LearningMaven

About

I see business as a puzzle involving capital, data, and decisions flowing across borders. My goal is to understand how these pieces fit together. My current dual degree with MDI Gurgaon and ESCP Europe is my lens for this, offering me a direct look into both India’s dynamic economy and Europe's diverse business landscape. Before business school, I was a Quantitative Analyst at Dolat Capital. The world of options trading sharpened my ability for quick problem solving and clear decision making under pressure. My engineering background from IIT Bhubaneswar is my analytical bedrock, teaching me to break down any challenge into its core parts and ask the right questions. I am currently exploring opportunities where business, technology, and creativity meet. I am most excited by work that is intellectually challenging and practically impactful. If you are working on something innovative, I would be very interested to learn more and connect.

Experience

1 yr 1 mo

Total Experience

1 yr 1 mo

Average Tenure

Current Experience

Dolat capital

Quantitative Analyst

Jul 2023 – Aug 2024 · 1 yr 1 mo · Mumbai, Maharashtra, India · On-site

Conducted in-depth analysis of financial market data to identify trends and patterns and implemented Python-based automation solutions, reducing data processing time by 75%.
Built pipelines for creating multiple linear regression models with automated preprocessing, data filtering, and feature selection from 2000+ indicators using Optuna, optimising R2, directional accuracy, and more.
Backtested the linear regression models on historical data to gauge performance and performed semi automatic trading involving activating/deactivating models server wise based on market scenario leveraging market indicators and past performance of model.
Developed a risk management module incorporating options Greeks, volume, and PnL; created visualisation scripts for debugging strategy orders in live markets using Python.
Designed and deployed two robust trading models, generating a cumulative net PnL of over INR 10 lakh+.
Conducted comprehensive back-testing of volatility-curve-based trading strategy in collaboration with the infrastructure team to resolve strategy and UI issues.

PythonQuantitative Analysis (Finance)Statistical ModelingData AnalysisDerivatives Trading

Ceremorphic, inc.

Digital Design Intern

May 2021 – Jul 2021 · 2 mos

Worked as a Digital Design intern at Ceremorphic and worked on 2 components used in pipelines for superscalar processors.
Implemented dynamic branch predictors like GShare and GSelect with set-associative mapped Branch Target Buffer and Least Recently Used cache replacement policy for RISC-V ISA using Verilog HDL.
Evaluated their performance using industry-standard benchmarks, notable ones being coremark and dhrystone.
To improve branch prediction during function returns, implemented a Return Address Predictor of length 4 using Verilog HDL.

VerilogDigital DesignRISC-VBranch Prediction

Crio.do

Product Developer

May 2020 – Jul 2020 · 2 mos

Worked as a Product Developer at Crio.do and worked on 3 guided projects intended to improve my backend web development skills
a)QBox: It is a secured file-sharing solution using VSFTPD to enable the transfer of files between clients
It was mostly intended to cover concepts like Linux, scripting, Operating Systems, and Network Foundations.
b)QMoney: It is an application based on Spring-boot which returned stock predictions based on the purchase date of the user, used OOPs and proper API practices to publish my code as a library to the local Maven Repository.
c)QEats: Worked on the backend of a food delivery application, Implemented the backend using MVCS architecture, made use of MongoDB to create databases and fetch data from those, extensive use of serialization/deserialization to send and fetch data from the server , also made use of Spring annotations while building REST APIs,used Mockito and Junit testing to test my code

Spring FrameworkMongoDBREST APIsLinuxJavaBackend Development