Austin Community College
Mid-Level Quantitative Researcher
Austin Community College, Stamford, Connecticut, United States, 06925
Mid-Level Quantitative Researcher
Location:
Stamford, CT
Team:
Relative Value Volatility Strategies
Reports to:
Deputy CIO
Overview We are seeking a mid-level quantitative researcher to join our investment team focused on relative-value volatility trading strategies across equities, indices, fx, rates, and credit. The role will combine quantitative modeling, data engineering, and applied research, supporting semi-systematic trading initiatives.
The ideal candidate will have strong Python programming skills, experience with cloud-based data platforms (Snowflake preferred), and a background in volatility products, derivatives, or other complex instruments.
Key Responsibilities
Research and implement relative-value volatility strategies, including spread, curve, and cross-asset vol relationships.
Build and enhance quantitative models for pricing, and signal generation.
Analyze historical and real-time market data to identify dislocations and arbitrage opportunities.
Work with large structured and unstructured datasets in Snowflake, ensuring data integrity and accessibility.
Develop Python-based research and production tools for backtesting, trade simulation, and performance attribution.
Collaborate across the team to translate research into executable strategies.
Present research findings in a clear, concise manner to senior stakeholders.
Qualifications
3–6 years
of experience as a quantitative researcher, strategist, or data scientist in a hedge fund, bank, or trading firm.
Solid understanding of volatility products and derivatives (e.g., options, variance swaps, VIX futures, volatility indices).
Proficiency in Python for research, modeling, and data pipelines.
Hands‑on experience with Snowflake or similar cloud-based data warehouses.
Strong quantitative background (statistics, econometrics, applied math, or financial engineering).
Familiarity with time-series modeling, machine learning, or risk factor analysis.
Ability to work in a fast-paced, collaborative, and entrepreneurial environment.
Preferred Skills
Experience with systematic volatility strategies (relative value, dispersion, correlation, skew).
Background in SQL, data engineering, and APIs for market data ingestion.
Exposure to portfolio construction, PnL attribution, and risk modeling.
Advanced degree (MS/PhD) in a quantitative field is a plus.
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Stamford, CT
Team:
Relative Value Volatility Strategies
Reports to:
Deputy CIO
Overview We are seeking a mid-level quantitative researcher to join our investment team focused on relative-value volatility trading strategies across equities, indices, fx, rates, and credit. The role will combine quantitative modeling, data engineering, and applied research, supporting semi-systematic trading initiatives.
The ideal candidate will have strong Python programming skills, experience with cloud-based data platforms (Snowflake preferred), and a background in volatility products, derivatives, or other complex instruments.
Key Responsibilities
Research and implement relative-value volatility strategies, including spread, curve, and cross-asset vol relationships.
Build and enhance quantitative models for pricing, and signal generation.
Analyze historical and real-time market data to identify dislocations and arbitrage opportunities.
Work with large structured and unstructured datasets in Snowflake, ensuring data integrity and accessibility.
Develop Python-based research and production tools for backtesting, trade simulation, and performance attribution.
Collaborate across the team to translate research into executable strategies.
Present research findings in a clear, concise manner to senior stakeholders.
Qualifications
3–6 years
of experience as a quantitative researcher, strategist, or data scientist in a hedge fund, bank, or trading firm.
Solid understanding of volatility products and derivatives (e.g., options, variance swaps, VIX futures, volatility indices).
Proficiency in Python for research, modeling, and data pipelines.
Hands‑on experience with Snowflake or similar cloud-based data warehouses.
Strong quantitative background (statistics, econometrics, applied math, or financial engineering).
Familiarity with time-series modeling, machine learning, or risk factor analysis.
Ability to work in a fast-paced, collaborative, and entrepreneurial environment.
Preferred Skills
Experience with systematic volatility strategies (relative value, dispersion, correlation, skew).
Background in SQL, data engineering, and APIs for market data ingestion.
Exposure to portfolio construction, PnL attribution, and risk modeling.
Advanced degree (MS/PhD) in a quantitative field is a plus.
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