Quantitative Trading Analyst ( Commodities)

Summit Dairy; Hyderabad; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Summit Dairy in Hyderabad invites a Quantitative Trading Analyst to join our trading analytics team. You will develop statistical models, forecast prices, and test signals for agricultural commodities, with exposure to futures and options.

The role requires 2–4 years in quantitative research and a strong foundation in statistics, econometrics, and Python. Work hours align with American market activity in the Hyderabad area.

Qualifications

• 2–4 years in quantitative research, risk, econometric modelling, or a trading analytics role.
• Prior exposure to commodities, futures, or options is a plus but not mandatory.
• Advanced degrees are desirable but not mandatory.
• Graduate from a top-tier institution in a quantitative field.

Responsibilities

• Build quantitative models for price forecasting, spread relationships, seasonality, and volatility across agricultural commodities.
• Apply time-series methods and regression-based fundamental models.
• Model supply-demand balances using production, trade-flow, inventory, and weather data.
• Design, back-test, and stress-test systematic and discretionary trading signals.

Skills

Probability & statistics

Econometrics

Time-series analysis

Regression diagnostics

Hypothesis testing

Trading interest

Education

Graduate or postgraduate degree

Tools

Python

R

MATLAB

Job description

Quantitative Trading Analyst Commodities (Trader Track)

Location: Hyderabad, India

Company: Summit Dairy

Employment type: Full-time

Working hours: 2:30/3:30 PM to 11:30 PM/12:30 AM IST (American market hours)

Role Overview

We are seeking to hire an analyst with a strong background in statistics, econometrics, or mathematics to join our trading and analytics team in Hyderabad. The role involves quantitative research to expand trading desk, with the opportunity to grow into a trading role over time.

This position requires the ability to work during American market hours (2:30/3:30 PM to 11:30 PM/12:30 AM IST) to align with trading activity.

Key Responsibilities

Quantitative research and modelling

• Build quantitative models for price forecasting, spread relationships, seasonality, and volatility across agricultural commodities.
• Apply time-series methods and regression-based fundamental models.
• Model supply-demand balances using production, trade-flow, inventory, and weather data.
• Design, back-test, and stress-test systematic and discretionary trading signals.

Analysis and reporting

• Validate hypotheses using structured and unstructured data.
• Prepare market notes, reports, and ad-hoc analysis for traders and management.
• Review strategy performance to refine models and process.

Trading Analytics

• Monitor markets, positions, and risk for the trading desk.
• Track scheduled market reports, data releases, and market-moving news.
• Support traders to validate hypotheses using quantitative models.

Candidate Profile

Education

• Graduate or postgraduate degree from a top-tier institution.
• Preferred fields: Statistics, Econometrics, Mathematics, Quantitative Economics, Financial Engineering, or related quantitative disciplines.
• Advanced degrees are desirable but not mandatory.

Experience

• 2-4 years in quantitative research, risk, econometric modelling, or a trading/analytics role; exceptional fresh graduates will be considered.
• Prior exposure to commodities, futures, or options is a plus but not mandatory.

Core attributes

• Genuine interest in trading and commodity markets, with the ambition to have a direct impact on P&L generation.
• Intellectual honesty: comfortable with model uncertainty and quick to discard ideas that fail.
• Proactive, self-driven, and composed in a fast-paced, high-responsibility environment.
• Team Player with a collaborative mindset

Desired Skill Set

• Strong foundation in probability, statistical inference, and econometrics, including regression diagnostics, time-series analysis, and hypothesis testing.
• Proficiency in Python for data analysis and modelling; R or MATLAB is a plus.
• Experience working with financial time series and building back-tests free of look-ahead and survivorship bias.
• Ability to explain quantitative findings clearly to traders and management.