Top Quantitative Researcher Jobs in NYC, NY

The Quantitative Researcher for Monetization will develop strategies for market opportunities, optimize performance using data, and apply machine learning in financial energy systems.

Lead expansion into new opportunities by building and deploying profitable high-frequency quantitative trading strategies. Manage a research team to develop pricing and execution models, oversee end-to-end deployment, and integrate large-scale ML/Ops and high-performance compute into trading systems.

Summer internship for graduate students to research and develop trading models from large datasets. Interns collaborate with senior quants and developers, complete one-to-two projects, receive training, and apply quantitative, mathematical, and programming skills (C/C++, Python, Pandas) to improve algorithmic trading strategies.

Undergraduate quant research intern to analyze financial datasets, clean and validate data, build simple models and backtests, write Python analysis and visualizations, and summarize results. Work collaboratively with researchers, traders, and engineers to develop and test systematic trading ideas.

Analyze execution data across equities, futures, and other asset classes to build and maintain market impact and trade-cost models. Develop tools and studies to improve execution, collaborate with traders, researchers, brokers, and operations, and present findings to senior researchers to reduce transaction costs and enhance portfolio performance.

Summer internship working with quantitative strategists to analyze large trading datasets, develop models to improve trading algorithms, complete one to two mentored projects, and participate in training and team events.

Develop and enhance data-driven trading strategies for US financial markets. Responsibilities include analyzing large datasets, generating predictive signals, collaborating with trading and engineering teams, and owning research and strategy development end to end. The role requires quantitative problem-solving, statistical analysis, programming, algorithms, and Linux/Unix expertise in a fast-paced research environment.

Develop sophisticated trading models using machine learning and data analysis. Manage the research process from data ingestion to model evaluation. Collaborate and contribute to all stages of financial model development leveraging deep learning.

The role involves conducting research to develop systematic macro strategies and signals focusing on market microstructure for trading. Responsibilities include feature engineering, modeling, backtesting, and improving trading environments.

Lead quantitative research, driving revenue and trading capabilities through collaboration with trading and technology, and applying rigorous scientific methods to solve business problems.

The Quantitative Researcher will analyze market data to build models and drive innovation in trading strategies, collaborating with traders and engineers.

Conduct quantitative research and develop convertible bond and credit trading strategies; design pricing and relative-value models; analyze corporate fundamentals and credit risk; execute trades and manage positions; build automated screening, monitoring, and risk tools; implement hedging and oversee portfolio risk and performance using internal APIs and historical market data.

Research and implement automated trading strategies across multiple asset classes and regions. Analyze large datasets with advanced statistical methods, design and implement simulation and backtesting components, and develop high-quality, independent research informed by market microstructure. Collaborate using version control and common development environments.

Develop econometric and statistical models to forecast asset price movements and macro variables, implement and validate algorithmic trading strategies, automate data processing in Python, monitor live trading automations and associated risk, and analyze macroeconomic releases and BLS datasets.

The Quantitative Researcher will develop and optimize trading strategies, analyze data for alpha signals, and collaborate on financial models.

As a Quantitative Researcher, you'll develop trading algorithms, analyze financial data, create predictive models, and collaborate within a research team.

The role involves designing systematic investment strategies using applied mathematics and statistics to create and improve trading algorithms.

The role involves identifying market opportunities, constructing models, developing analyses, implementing, and testing models in trading systems, while collaborating with traders and developers.

Nine-week summer internship working with the research team on development projects. PhD-level quantitative problem solving, mentorship, classroom instruction, strategy games, and hands-on projects. Requires strong math, basic programming experience, teamwork, independence, and attention to detail.

Develop and optimize machine learning and statistical models to predict liquid asset movements. Parse and analyze large datasets, implement academic research for alpha generation, and collaborate with Data, Alpha, and Strategy teams to design market-neutral trading strategies and run simulations to validate hypotheses.

Develop systematic trading models, conduct alpha idea generation, backtesting, and enhance trading environments while utilizing advanced quantitative methods.

As a Quantitative Researcher, you will develop and implement quantitative trading signals and models, using statistical methodologies to evaluate research systems components.

Conduct execution-quality research across global markets: model market impact and slippage, design A/B tests and causal analyses, build optimization and simulation tools, and deliver analytics and decision support to traders and PMs for improved order routing and execution.

As a Quantitative Researcher at Jane Street, you will build models, analyze datasets, create strategies for trading, and write implementation code, primarily in OCaml.

The role involves conducting quantitative research in corporate bonds and credit derivatives, implementing models, and improving investment processes.