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Quant Associate

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About Us: OptionMetrics, headquartered in New York, NY. is a dynamic and innovative technology company, a trusted provider of financial information and research derived from the option markets. We are at the forefront of developing solutions that empower businesses. Our commitment to excellence, coupled with a collaborative, forward-thinking culture, has made us the preferred choice for top-tier talent. Our data and analytics models are utilized by over 350 investment banks, hedge funds, asset management firms, and academic institutions globally, solidifying our position as a leader in the industry. At OptionMetrics, we are seeking a curious, analytical, and detail-oriented Quant Associate to join our Quantitative Research team. This is an excellent opportunity for an early-career quantitative professional who is passionate about financial modeling, derivatives, and applied research. As a Quant Associate, you will partner closely with our Senior Quantitative Researchers on a variety of quantitative research initiatives, contributing to the development and enhancement of our industry-leading options analytics platform. You will have the opportunity to work on challenging problems, develop innovative models, and see your work directly impact our products and clients. We are a small, agile organization that values intellectual curiosity, collaboration, and craftsmanship. We emphasize quality over expediency and encourage our team members to take ownership of their work from concept through implementation. You\'ll be surrounded by exceptionally bright colleagues who are passionate about solving complex financial problems and advancing quantitative research. Location: Hybrid (New York City) What You\'ll Do: Assist in the development, implementation, and maintenance of the firm\'s option pricing models and quantitative analytics. Partner with Senior Quantitative Researchers on research initiatives and model enhancements. Contribute to the development of new products, quantitative tools, and analytics. Collaborate with the Head of Quantitative Research to understand client needs and help develop innovative quantitative solutions. Conduct statistical analysis and quantitative research using large financial datasets. Validate, test, and improve pricing models to ensure accuracy, robustness, and performance. Document research findings and communicate technical concepts clearly to both technical and non-technical audiences. Skills You\'ll Need: Master\'s degree (M.S.) or higher in Financial Engineering, Quantitative Finance, Econometrics, Statistics, Applied Mathematics, Physics, Computer Science, or another highly quantitative discipline. Strong interest in derivatives, option pricing, financial markets, and quantitative modeling. Proficiency in at least one programming language such as Python, C++, or Java. Strong analytical and problem-solving skills with exceptional attention to detail. Excellent written and verbal communication skills. Ability to manage multiple priorities while working both independently and collaboratively. What Will Make You Stand Out: Academic or research experience in derivatives, stochastic calculus, numerical methods, or financial modeling. Experience implementing quantitative models in Python or C++. Familiarity with options pricing models such as Black-Scholes, binomial trees, Monte Carlo simulation, or finite difference methods. Experience working with large financial datasets and statistical analysis. Exposure to machine learning techniques or predictive modeling. Internship or research experience within quantitative finance, financial engineering, or capital markets. Intellectual curiosity, a desire to continuously learn, and enthusiasm for tackling complex quantitative challenges. What We Offer: A collaborative environment where everyone\'s input makes a difference. Paid time off: Vacation, Personal, Sick days, and Holidays. Pre-tax commuter benefits (NJT, MTA, etc.) and 401k plan offered. Full medical and dental insurance coverage. #J-18808-Ljbffr Jobless

Vacancy posted 4 days ago
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