Ashutosh Ranjan

AI Researcher

Bengaluru, Karnataka, India7 yrs experience

Key Highlights

Expert in quantitative finance and algorithmic trading.
Developed innovative data-driven solutions for financial markets.
Recognized for impactful contributions to data science projects.

Stackforce AI infers this person is a Fintech professional with expertise in quantitative analysis and data-driven trading strategies.

Contact

ashutosh.ranjan019@gmail.com LinkedIn

Skills

Core Skills

Quantitative FinanceAlgorithmic TradingData Science

Other Skills

AlgorithmsAutoencodersBashCC++Cascading Style Sheets (CSS)Commodity FuturesCommunicationComputational LinguisticsDerivatives TradingFX FuturesGMMGlobal MacroHTMLIndex Futures

About

Deep diving into data, one problem at a time.

Experience

Zepto

2 roles

Lead Data Scientist

Promoted

Oct 2025 – Present · 5 mos · Bengaluru, Karnataka, India

Senior Data Scientist

Oct 2024 – Sep 2025 · 11 mos · Bengaluru, Karnataka, India

Last Mile, Maps, Network Optimization

Alphagrep securities

Quantitative Trader

Jun 2021 – Apr 2024 · 2 yrs 10 mos · Bengaluru, Karnataka, India

As a mid frequency quant trader, I designed, developed and traded statistical models in markets such as global macro and NSE.
Spent most of my tenure building the global macro desk trading US treasury bond futures, index futures, commodity futures (softs, grains, energy) and fx futures on CME and ICE as part of teams Boson and Emergence.
Used Stats, OR, ML and NLP to research alphas from price, sentiment and other alternative data.

Statistical ModelsGlobal MacroNSEUS Treasury Bond FuturesIndex FuturesCommodity Futures+7

Intuit

3 roles

Software Engineer

Jul 2020 – Jun 2021 · 11 mos

Data Scientist

Jan 2020 – Jul 2020 · 6 mos

Intuit AI IDC
Subscription Insights: Discover subscriptions for Mint users, dynamically, and notify them about their spending trends. CTR: 10.5%. User coverage: 1.5M. Received Spotlight award for this work. (PySpark, python)
Budget Recommendation: Based on historic transactions of Mint users propose new budgets to users likely to exceed their budget in a given month. CTR: 6.4%. User coverage: 800k. (Random Forest)
Mint User Clustering: Clustering millions of Mint users into specific categories based on their spend patterns. Using autoencoder to reduce dimensionality before clustering. (autoencoders, GMM)

Data Scientist

May 2019 – Jul 2019 · 2 mos · Bengaluru, Karnataka, India

Central Data Organisation - Intuit AI IDC
Worked towards - "Intelligent Topic Extraction from User Reviews" - as a part of the market analysis project Cerebro.
Prototyped a domain-specific natural language search engine for QuickBooks, increasing customers' speed of accessibility by 10 times.

Iiit hyderabad

2 roles

Teaching Assistant

Aug 2018 – Dec 2018 · 4 mos · Hyderabad Area, India

Computational Linguistics course by Prof. Dipti Misra Sharma

Research Assistant

Jul 2017 – Aug 2018 · 1 yr 1 mo · Hyderabad Area, India

Proposed and simulated a mechanism to increase rank-efficiency and stability in dynamic matching scenarios under overall system constraints.
Mentored by Prof. Sujit Gujar (ML Lab, IIIT Hyderabad) and Prof. Vinay Ramani (IIM Udaipur).