דרושים Quantitative Modeling ב-גיי פי מורגן ב-United States, New York

Job Description:

As a Site Reliability Engineer II at JPMorgan Chase within the Technology Infrastructure Services, you will solve complex and broad business problems with simple and straightforward solutions. Through code and cloud infrastructure, you will configure, maintain, monitor, and optimize applications and their associated infrastructure to independently decompose and iteratively improve on existing solutions. You are a significant contributor to your team by sharing your knowledge of end-to-end operations, availability, reliability, and scalability of your application or platform.

Job Responsibilities:

• Develop and implement strategies to enhance the reliability, availability, and scalability of applications and platforms.
• Collaborate with cross-functional teams, including software engineers, product managers, and stakeholders, to align SRE practices with business objectives.
• Oversee the design and implementation of automated continuous integration and continuous delivery pipelines.
• Manage and resolve complex production-related issues, ensuring minimal impact on customers and business operations.
• Foster a culture of site reliability engineering best practices, promoting proactive issue resolution and continuous improvement.
• Communicate effectively with stakeholders, providing updates on system performance, incidents, and improvement initiatives.
• Identify and implement new technologies and solutions to enhance the SRE function and support business growth.

Required Qualifications, Capabilities, and Skills:

• 5+ years of experience in site reliability engineering or a related field, with a proven track record of managing and leading technical teams.
• Strong understanding of site reliability engineering principles and practices, with the ability to implement them within an organization.
• Excellent communication and interpersonal skills, with the ability to engage and influence stakeholders at all levels.
• Proficient in at least one programming language such as Python, Java/Spring Boot, or .Net.
• Experience with observability tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, and others.
• Familiarity with continuous integration and continuous delivery tools like Jenkins, GitLab, or Terraform.
• Experience with container and container orchestration technologies such as ECS, Kubernetes, and Docker.
• Strong problem-solving skills, with the ability to manage and resolve complex production-related issues.
• Ability to identify and implement new technologies and solutions to support business objectives and drive innovation.

Preferred Qualifications:

• Formal training or certification in SRE concepts and practices.
• Experience in a financial services or highly regulated industry.
• Demonstrated ability to lead and manage large, collaborative teams in a fast-paced environment.

Same Posting Description for Internal and External Candidates

As a Quantitative Researcher- Alpha Quant in the Cash Equities team, you will focus on data processing, signal feature construction, signal research, and systematic trading. You will help to drive the alpha research agenda for central risk book trading, using data analytics and programming to drive business transformation. Your role will involve feature engineering from various data sources, building robust alpha calibration and attribution framework, portfolio optimization, develop prediction models, collaborating with trading desks and implementing trading strategies.

Job Responsibilities:

• Work closely with trading to build end-to-end design and implementation of daily and intraday signal research infrastructure, with special focus on central risk trading.
• Contribute from idea generation to production implementation: perform research, design prototype, implement signals, alpha calibration, and trading strategies, support their daily usage, and analyze their performances.
• Develop robust portfolio optimization framework including drawdown management for signal weighting and maximizing various utility functions.
• Research on factor models driving the cash and vol markets; Analyze factors and strategy performance under different market regimes.
• Develop models for market making taking into consideration fundamental and quantitative features, and historic behavior using statistics, machine learning or heuristics.
• Work with the business on risk recycle, alpha capture, and devise hedging strategies accordingly.
• Collaborate broadly with QR teams across regions to build reusable research libraries and tools to back testing and alpha attributions.

Required Qualifications, Capabilities, and Skills:

• You have a strong quantitative background, as well as practical problem-solving skills.
• You have direct working knowledge of signal research with market data and other financial data, alpha capture, risk warehousing, preferrable in equities.
• You like working closely with trading desks, understanding their business, and have a strong mind-set of ownership to have an impact on the way they operate.
• You demonstrate proficiency in code design and programming skills, with primary focus on Python, KDB, C++ or Java in a commercial environment.
• You have practical data analytics skills on real data sets gained through hands-on experience, and can handle and analyze complex, large scale, high-dimensionality data from various sources.
• You quickly grasp business concepts outside immediate area of expertise and adapt to rapidly changing business needs.
• You think strategically and creatively when faced with problems and opportunities. You always look for new ways of doing things.
• Your excellent communication skills, both verbal and written, can engage and influence partners and stakeholders.

Preferred Qualifications, Capabilities, and Skills:

• Strong graduate degree (MS or PhD) in a quantitative field (Computer Science, Financial Engineering, Mathematics, Physics, Statistics, Economics, …).
• Strong expertise in statistics and machine learning in financial industry.
• Robust testing and verification practice.
• Direct Experience with electronic trading, and knowledge of trading algorithms.
• 3 to 5 years’ experience in finance: market making, electronic trading, , trading strategies (high to low frequency: market making, statistical arbitrage, option trading…), or derivatives pricing and risk management.

Knowledge of equity and volatility products is a plus, but not strict r

As a Quant Model RiskVice Presidentin the Model Risk Governance and Review team, you will be responsible for assessing and mitigating the risks associated with complex models used for valuation, risk measurement, capital calculation, and decision-making purposes. .You'll be at the forefront of innovation, driving continuous improvement in a dynamic and collaborative environment

You will also have managerial responsibility to oversee, train and mentor junior members of the team.

Job Responsibilities

• Perform thorough reviews of complexcredit, interest rate, and equitypricing models, including valuation engines and reserve methodologies. Analyze the conceptual soundness, model design, and appropriateness of models for specific products and structures.
• Evaluate model behavior and ensure the suitability of pricing models and engines for their intended applications, identifying potential limitations and areas for improvement.
• Develop and implement alternative model benchmarks. Design and maintain robust model performance metrics to compare and monitor the outcomes of various models.
• Continuously evaluate model performance, ensuring models remain fit for purpose and compliant with internal and regulatory standards. Recommend enhancements and oversee remediation where necessary.
• Serve as the primary point of contact for the business regarding new model implementations and changes to existing models. Provide expert guidance on model usage, limitations, and governance requirements.
• Liaise effectively with model developers, Risk, and Valuation Control Groups. Offer guidance and support on model risk management, validation standards, and regulatory expectations.
• Manage and develop junior team members, providing mentorship, guidance, and support to foster their professional growth and enhance overall team performance.

Required Qualifications, Capabilities and Skills

• Advanced degree (MSc, PhD, or equivalent) in a quantitative discipline such as mathematics, statistics, financial engineering, or related field.
• Advanced knowledge of probability theory, stochastic processes, statistics, partial differential equations, and numerical analysis, with demonstrated ability to apply these concepts to financial modeling and risk assessment.
• Deep understanding of option pricing theory and quantitative models for pricing and hedging derivatives, including familiarity with stochastic calculus and risk-neutral valuation.
• Strong analytical and problem-solving skills, with an inquisitive mindset and the ability to formulate insightful questions, identify model limitations, and escalate issues appropriately.
• Excellent written and verbal communication skills, with the ability to clearly explain complex quantitative concepts to both technical and non-technical stakeholders.
• Proficient programming skills in languages such as C/C++, Python, or similar, with experience implementing numerical algorithms and developing model prototypes.
• Demonstrated curiosity and ownership, with a strong willingness to work collaboratively within a team-oriented environment.
• Extensive experience in front office model development or in model review, validation, and governance within financial services, with a strong understanding of credit, interest rate, and equity pricing models.

Job Summary:

As a Vice President in the Quantitative Research SPG team, you will play a pivotal role in supporting the Global Securitized Product Group (SPG) business. Your responsibilities will include leading the development, documentation, and enhancement of advanced quantitative models and analytical tools for SPG. You will collaborate with the business, risk, and model review teams to support proper model usage, maintain infrastructure, and provide expert guidance and training to users and clients.

Job Responsibilities:

• Lead the development and maintenance of advanced models for valuation, risk assessment, profit and loss (P&L) calculations, as well as algorithms for quoting and market making, utilizing sophisticated mathematical approaches.
• Ensure comprehensive documentation of all new models to comply with firm-wide model risk policies and procedures.
• Design and implement analytical tools to monitor market conditions in Residential Mortgage-Backed Securities (RMBS), enhancing decision-making processes.
• Conduct data queries and processing for RMBS prepayment and credit modeling, ensuring high-quality data analysis at the loan or facility level.
• Investigate and develop new techniques to improve mathematical and computational efficiency within modeling processes.
• Ensure appropriate model usage across a diverse range of business users and risk functions, providing guidance and training as needed.
• Build and optimize a robust platform for large-scale data analysis to support various modeling initiatives.
• Develop a new model library focused on achieving desired computational efficiency and usability.
• Oversee the maintenance and enhancement of existing infrastructure used for valuing and hedging financial transactions.
• Work closely with risk and model review groups to ensure proper model usage, conduct model reviews, and implement effective risk controls.
• Provide support to internal and external clients regarding their model usage, addressing inquiries and facilitating training as needed.

Required qualifications, capabilities, and skills:

• 3+ years of experience at the Vice President level.
• Proficient in Python and C++ for developing analytical tools and models.
• Skilled in working within a Linux shell environment, utilizing shell scripting for automation and data processing.
• Extensive experience in data analysis focused on mortgage and loan performance datasets, specifically analyzing prepayment and credit historical data at the loan or facility level.
• Expertise in developing econometric models to assess financial performance and risk.
• Proficient in conducting Monte Carlo simulations for risk analysis and forecasting.
• Experienced in developing logistic regression models to predict binary outcomes based on historical data.
• Knowledgeable in developing factor models, including Principal Component Analysis (PCA) and hazard rate models.
• Proficient in using statistical Python packages such as NumPy, Pandas, and StatsModels for data manipulation and statistical analysis.

Job Responsibilities:

• Participate in the development of J.P.Morgan systematic trading strategies with our Structuring teams
• Develop, deploy, and maintain new and existing algorithmic trading strategies
• Expand and support the risk management platform used by traders to hedge investable indices traded with our clients
• Build foundational infrastructure to support new product offerings, enhance efficiency, and improve controls
• Provide support to Trading teams through risk analysis and investigations of production trading strategies
• Contribute to our automation ecosystem by delivering end-to-end automation and optimization of trading execution and other related investable index trading and risk management workflows

Required qualifications, capabilities, and skills:

• You hold an advanced degree (Master’s or PhD) in a quantitative field: Mathematics, Computer Science, Physics, Engineering (or equivalent)
• You have experience working with quantitative investment strategies and derivatives, ideally with cross-asset exposure to Equities, Commodities, and/or Rates
• You have a strong programming background with high proficiency in Python
• You have highly-focused attention to details and to the quality of deliverables
• You understand advanced mathematics used in financial modeling including topics such as calculus, numerical analysis, optimization, and statistics
• You have a good understanding of the mathematics involved in the valuation of financial products and trading strategies
• You demonstrate exceptional analytical, quantitative, and problem-solving skills
• Your excellent communication skills, both verbal and written, can engage partners and stakeholders on complex and technical topics, which you can explain with exceeding clarity

Preferred qualifications, capabilities, and skills.

• Experience of financial markets and familiarity with general trading concepts and terminology
• Knowledge of derivatives pricing theory, trading algorithms, and/or financial regulations
• You understand the different types of financial risk and you can discuss in detail ways of managing these risks
• You are interested in applying agile development practices in a front-office trading environment
• You have good practical knowledge of derivatives pricing and risk management of vanilla options and volatility products
• A mindset of robust system and solution design and implementation, including diligent testing and verification practices

Job Summary

As a Quantitative Finance Summer Analyst & Associate in the Quantitative Finance Program, you will work alongside top-tier professionals in a dynamic environment. You’ll apply academic knowledge to real-world financial challenges, gain hands-on experience, and build a valuable network. This program sets a solid foundation for your career, with potential full-time offers upon successful completion.

Job Responsibilities:

• Analyze data to identify patterns, revenue opportunities, and market trends.
• Conduct back testing and assess risk management strategies.
• Maintain and improve software systems and tools for trading operations.
• Assess models for conceptual soundness, risks, and enhancements.
• Propose creative solutions to complex challenges.
• Collaborate with internal teams to advance trading services.
• Focus on model development and review of conceptual design.
• Develop, validate, and enhance mathematical models and algorithms.
• Optimize financial solutions across asset classes and instruments.

Required qualifications, capabilities, and skills:

• Enrolled in a Bachelor’s, Master’s, or PhD program in mathematics, statistics, physics, engineering, computer science, economics, or data science/machine learning, graduating between December 2026 and August 2027.
• Proficiency in Python, C++, or Java.
• Strong analytical, quantitative, and problem-solving skills.
• Excellent communication skills for presenting complex concepts.
• Interest in banking analytics, global markets, and quantitative research.
• Ability to thrive in a fast-paced, collaborative environment.

Preferred qualifications, capabilities, and skills

• Experience with R, MATLAB, or SQL.
• Familiarity with data visualization tools like Tableau or Power BI.
• Understanding of banking products, financial instruments, and market dynamics.
• Strong organizational skills for managing multiple projects.
• Ability to articulate complex quantitative concepts to diverse audiences.

Locations:

• New York Metro, NY
• Plano, TX
• Chicago, IL

Quantitative Analytics Analyst & Associate:

• Role: Work on developing complex solutions that support JPMorganChase's global business. Engage in financial engineering, derivatives modeling, asset and liability management, and risk management.
• Team Tasks: Develop or validate mathematical models, methodologies, and tools. Collaborate across the organization to solve problems and propose innovative solutions.

Quantitative Research Analyst & Associate:

• Role: Develop sophisticated financial solutions across asset classes to help clients manage risk, increase returns, and solve complex financial problems. Engage in quantitative modeling, data analytics, statistical modeling, and portfolio management.
• Team Tasks: Collaborate with quants, technologists, traders, marketers, and risk managers to enhance models and algorithms. Calibrate parameters for market evolution models, optimize pricing, manage portfolio risks, and develop statistical arbitrage strategies. Maintain mathematical models and methodologies for valuing and hedging financial transactions, and improve algorithmic trading strategies.

Asset Management Analyst & Associate:

• Role: Apply cutting-edge quantitative investment and data science techniques across a broad range of applications and data types. Create solutions for corporate, government, not-for-profit, and individual clients worldwide. Work in product teams including equities, fixed income, multi-asset solutions, real assets, hedge funds, and private equity. Projects can include developing innovative asset return prediction strategies; now-casting economic conditions; and estimating risk sensitivities.
• Team Tasks: Collaborate with investors, client portfolio managers, traders, research analysts, marketers, and client advisors to develop and implement investment strategies. Engage in assignments that directly impact clients, helping them achieve their business goals. Network with industry leaders and access best-in-class training to sharpen technical skills and finance principles.

We will review summer internship applications as they are received and extend offers on a rolling basis. We strongly encourage you to apply early, as programs will close as positions are filled.

Job responsibilities

• Work with business leads to develop AI/ML-driven analytics and automation that support their business goals
• Perform large-scale analysis on our proprietary dataset to solve problems never tackled before
• Test ideas, figure out what works, and write production code to make that idea work for the business
• Make real-world, commercial recommendations through effective presentations to various stakeholders
• Work closely with colleagues in Quantitative Research, Technology and the Chief Data and Analytics Office (CDAO) to drive the Securities Services data strategy forward

Required qualifications, capabilities, and skills

• Advanced degree (PhD or MS) or equivalent in a quantitative field: Physics, Mathematics, Computer Science, Engineering, etc.
• Robust understanding of Machine Learning, Statistics, and Mathematics, both in fundamentals as well as in application.
• Experience in tackling real world data science problems, end-to-end from prototype to production, using Python.
• Excellent communication skills (both verbal and written) and the ability to present findings to a non-technical audience.
• Passion for learning, sharing knowledge, building collaborations, and getting things done.

Preferred qualifications, capabilities, and skills

• Experience in applying LLMs and/or deep learning methods to solve business problems.
• Experience in working with Cloud and/or HPC environments.