Quantitative Research Lead / Quant Trading Bot Dev US Stock Market

2palpha; United States; Remote; USD 60,000 - 80,000; Full time

Job summary

A quantitative trading startup is seeking a Quantitative Research Lead / Quant Trading Bot Developer. This remote position involves building and optimizing trading models using Python, and developing real-time trading bots. The ideal candidate has a strong foundation in statistics and programming, and will benefit from competitive compensation including profit sharing and opportunities for growth.

Benefits

Competitive base salary

Profit-sharing on net profits

Opportunity to build systems from scratch

Direct exposure to US markets

Flat structure and fast-track growth

Qualifications

• Strong Python programming skills are required.
• Solid foundation in statistics and probability is essential.
• Experience with backtesting frameworks and APIs is a must.

Responsibilities

• Set up the quant research stack, focusing on data ingestion and model testing.
• Develop risk models and stock trading algorithms.
• Maintain accurate logs and strategy metrics.

Skills

Python programming

Statistics

Time-series analysis

Backtesting frameworks

Clean code writing

Education

Degree in Math, Stats, CS, or Quantitative Finance

Tools

Interactive Brokers

AWS

GCP

Docker

Kubernetes

Job description

Quantitative Research Lead / Quant Trading Bot Developer US Stock Market

Position: Full-time

Location: Remote / WFH (US market overlap hours)

Compensation: Competitive base salary + profit sharing from live strategies

About Us

Quant 2P Alpha is a quantitative trading and research startup focused on the US stock market. We are building our quant stack from the ground up—from data research pipelines to execution systems. This role offers a unique opportunity to be the first quant hire and play a critical role in shaping our strategy, models, and infrastructure.

Role Overview

We are hiring a Quantitative Researcher / Quant Developer to:

• Build the research and execution framework from scratch.
• Design and backtest systematic trading models.
• Develop real-time trading bots using broker APIs (e.g., Interactive Brokers).
• Implement statistical, ML, and time-series models for signal generation.
• Optimize for latency, execution cost, and risk-adjusted returns.
• Ensure backtests replicate accurately in live trading.

Required mindset: Bring an entrepreneurial mindset to take ownership, solve problems independently, and thrive in a startup environment.

Key Responsibilities

• Set up the quant research stack (data ingestion, feature engineering, model testing).
• Develop risk models, execution of stock trading algorithms (Algo), and monitoring systems.
• Maintain accurate logs, PnL tracking, and strategy metrics.
• Collaborate on new research in market inefficiencies and ML-based models.

Required Skills

• Strong Python programming (NumPy, pandas, vectorized ops).
• Solid foundation in statistics, probability, and time-series analysis.
• Experience with backtesting frameworks and APIs (Interactive Brokers preferred).
• Knowledge of PnL, slippage, risk-reward, profit factor.
• Ability to write clean, production-ready code.

Preferred Qualifications

• Degree in Math, Stats, CS, or Quantitative Finance (IIT, IISc, ISI, NIT, BITS, IIIT, CMI, DSE, Ashoka, or global equivalents).
• Experience in machine learning, data mining, or competitive programming.
• Participation in Kaggle, Codeforces, or similar competitions.
• Exposure to cloud platforms (AWS, GCP) and containerization (Docker, Kubernetes).

• Familiarity with stock markets or trading concepts (PnL, order execution, market data) is a plus but not required.

What We Offer

• Competitive base salary.
• Profit-sharing (7 - 10%) on net profits from deployed strategies.
• Opportunity to build systems from scratch with full ownership of your models.
• Direct exposure to US markets and institutional-grade data.
• Flat structure, close mentorship, and fast-track growth.

Selection Process

• Quantitative & Coding Assessment (Math, ML, Python, Time-Series).
• Technical interviews with founding team.