Quantitative Researcher

Company DescriptionQuant Insight offers solutions tailored for portfolio managers and risk officers to systematically manage macro exposure and enhance alpha generation. The company integrates intelligence about the broader macroeconomic forces impacting investment strategies without altering existing workflows. Quant Insight empowers professionals with tools that support informed decision-making and increase confidence in their market analysis. Experience: 1–2 yearsWe’re seeking a Quantitative Researcher to join our ETF and algorithmic trading research team. The ideal candidate is passionate about markets, statistics, and systematic strategy design, with hands-on experience in quantitative research or trading environments.What you'll do:Research, design, and backtest systematic strategies across ETF universes (US & global).Develop factor-based and statistical arbitrage models using cross-sectional and time-series regressions.Apply dimensionality-reduction techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) to identify latent risk factors, common drivers, and co-movements across ETFs and underlying assets.Perform multi-factor regressions to evaluate exposures to macro, sectoral, and liquidity factors.Work on mean-reversion, momentum, and pair-trading models across ETFs and index constituents.Collaborate with engineers and traders to translate research into executable trading signals.Optimize code and backtesting pipelines in Python (and optionally C++) under Linux.Validate results through out-of-sample testing, cross-validation, and performance attribution.What We’re Looking For:1–2 years of experience in quantitative research, systematic trading, or financial data science.Proficiency in Python (NumPy, pandas, statsmodels, scikit-learn); C++ is a strong plus.Solid grounding in regression modelling, PCA/PLS, and statistical learning techniques.Understanding of ETF mechanics, tracking error, and index replication strategies.Familiarity with time-series analysis, volatility modelling, and signal decay.Strong problem-solving mindset and experience working in Linux environments.Degree in Mathematics, Statistics, Physics, Computer Science, or Finance (MSc or higher preferred).If you feel you are good fit for the role, don't hesitate to apply, and a consultant will follow-up with you soon!