Quantitative Insurance Solutions Analyst Jobs

ABOUT CUBIST Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. ROLE Entry-Level Quantitative Researchers are responsible for conducting rigorous quantitative research with a focus on predictive models. You will be trained in all aspects of systematic trading from idea generation all the way to practical trading considerations. Successful hires will ultimately become thought leaders within our collaborative research group. RESPONSIBILITIES Conduct original quantitative alpha signal research Follow, digest and analyze the latest academic research Manage all aspects of the research process, including idea generation, data analysis, hypothesis development and testing, alpha discovery, trading strategy generation, backtesting and portfolio analysis Build analytical tools to supplement our shared research framework REQUIRMENTS B.S., M.S. or PhD in finance, economics, mathematics, statistics, data science, computer science, or other quantitative discipline. Programming in Python (or comparable language) and working knowledge of SQL Strong analytical and quantitative skills. Willingness to take ownership of his/her work. Ability to work both independently and collaboratively within a team. Strong desire to deliver high quality results in a timely fashion. Detail-oriented. Prior experience in the financial services industry is not required. A commitment to the highest ethical standards.

ABOUT CUBIST Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. ROLE Entry-Level Quantitative Researchers are responsible for conducting rigorous quantitative research with a focus on predictive models. You will be trained in all aspects of systematic trading from idea generation all the way to practical trading considerations. Successful hires will ultimately become thought leaders within our collaborative research group. RESPONSIBILITIES Conduct original quantitative alpha signal research Follow, digest and analyze the latest academic research Manage all aspects of the research process, including idea generation, data analysis, hypothesis development and testing, alpha discovery, trading strategy generation, backtesting and portfolio analysis Build analytical tools to supplement our shared research framework REQUIRMENTS B.S., M.S. or PhD in finance, economics, mathematics, statistics, data science, computer science, or other quantitative discipline. Programming in Python (or comparable language) and working knowledge of SQL Strong analytical and quantitative skills. Willingness to take ownership of his/her work. Ability to work both independently and collaboratively within a team. Strong desire to deliver high quality results in a timely fashion. Detail-oriented. Prior experience in the financial services industry is not required. A commitment to the highest ethical standards.

ABOUT CUBIST Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. ROLE Entry-Level Quantitative Researchers are responsible for conducting rigorous quantitative research with a focus on predictive models. You will be trained in all aspects of systematic trading from idea generation all the way to practical trading considerations. Successful hires will ultimately become thought leaders within our collaborative research group. RESPONSIBILITIES Conduct original quantitative alpha signal research Follow, digest and analyze the latest academic research Manage all aspects of the research process, including idea generation, data analysis, hypothesis development and testing, alpha discovery, trading strategy generation, backtesting and portfolio analysis Build analytical tools to supplement our shared research framework REQUIRMENTS B.S., M.S. or PhD in finance, economics, mathematics, statistics, data science, computer science, or other quantitative discipline. Programming in Python (or comparable language) and working knowledge of SQL Strong analytical and quantitative skills. Willingness to take ownership of his/her work. Ability to work both independently and collaboratively within a team. Strong desire to deliver high quality results in a timely fashion. Detail-oriented. Prior experience in the financial services industry is not required. A commitment to the highest ethical standards.

About the Team: A well-established quantitative portfolio management team at Point72 is looking for an experienced quantitative professional to develop and trade systematic macro strategies, with a focus on market microstructure. The candidate will be given the resources and support to drive the build out and expansion of the quantitative macro business. Role/Responsibilities: Perform rigorous and innovative research to develop systematic signals for global macro (futures, FX, etc.) markets, with a focus on market microstructure signals Perform feature engineering with order book tick data at intraday to daily horizons Perform feature combination using various modeling techniques ranging from linear to machine learning models Participate in the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation Help drive the growth of the investment process and research capabilities of the team Work in a team of highly qualified and motivated individuals with access to a cutting-edge research and trading infrastructure and clean datasets Assist in building, maintenance, and continual improvement of production and trading environments Requirements: MS or PhD in physics, engineering, statistics, applied math, quantitative finance, or other quantitative fields with a strong foundation in statistics 4+ years of experience in quantitative research, building statistical models for intraday to daily trading, as part of a successful proprietary trading team with a track record Knowledge of market microstructure for futures and/or FX Prior experience with tick data based feature generation, modelling, and monetization Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas Collaborative mindset with strong independent research abilities Commitment to the highest ethical standards

VP Risk & Quantitative Analysis — 2 Locations. Apply via Workday.

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose. We are accepting applications for this position until 10/16/2026 Position Overview: At Freddie Mac, you will have meaningful work to help build a better housing finance system and support homeownership and rental housing opportunities across the nation. Our internship and graduate programs provide opportunities to tackle complex challenges, contribute to strategic initiatives, and build valuable relationships with industry professionals. As a Quantitative Risk Management Intern within Enterprise Risk Management (ERM), you will apply advanced analytical, technical, and quantitative skills to support risk management activities at one of the nation's largest financial institutions. You will gain hands-on experience working on real-world projects, collaborating across teams, and leveraging emerging technologies – including artificial intelligence and automation—to enhance risk management practices and business outcomes. Our Impact: Enterprise Risk Management (ERM) helps build and maintain a strong, effective, and efficient risk management framework across Freddie Mac. We provide independent oversight and assessment of financial and non-financial risks while promoting a culture of accountability, innovation, and sound risk management. Our quantitative teams leverage advanced analytics, data science, modeling, automation, and emerging technologies to support enterprise-wide decision-making and risk oversight. As AI and automation continue to transform the financial services industry, our teams p

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose. Position Overview: This is a two-year, full-time rotational program crafted to provide analysts with a broad spectrum of quantitative and technical experience working across multiple capital markets desks. The primary objective of this program is to develop modelers, desk quants and mortgage analytic practitioners through real-world training experiences spanning the entire mortgage capital markets life cycle. Analysts will accelerate their professional growth by working alongside best-in-class professionals in quant-focused rotations while also learning about and developing skills on fixed income trading desks. The Quantitative Track of the Portfolio Management & Capital Markets Analyst Program is seeking candidates with quantitative skills (i.e., mathematics/statistics/engineering/computer science, some level of programming skills, and other related STEM skills), with interest in fixed income and finance, having experience as a plus. Quantitative Track Analysts will rotate among quantitative-focused capital markets and credit portfolio teams and develop experience working with large data sets, performing complex quantitative analysis, and building programs using coding languages including, but not limited to, Python, SQL, R, SAS, and. Some of the rotations offered include: Demonstrating big data techniques with Prepayment Modeling to predict borrower behavior. Supporting Credit Risk Transfer desk in analytics related to Freddie Mac portfolio of loans. Modeling impacts of proposed products, pricing adjustments, or changes to our capital structure. Supporting the

Role: Point72 is looking for a Quantitative Researcher to join its Fund Flow Research team. The Fund Flow Group provides best in class flow and positioning indicators to help PMs, analysts, and traders across all investment functions understand their investment backdrop, better assess reward and risk, and identify alpha opportunities. Responsibilities : Create systematic trading strategies for macro and equity markets using proprietary flow and positioning datasets Conduct rigorous applied research to develop systematic signals for macro and equity markets, with a focus on investor flows and positioning Contribute to all aspects of the research and production process, including idea generation, hypothesis testing, portfolio construction, risk and transaction cost models, and P&L attribution Monitor, analyze and improve live trading P&L Collaborate with analysts and strategists to improve PM-facing content Requirements: Undergraduate, Master’s or PhD candidates in a technical field Demonstrated creativity and rigor in the research process Technically comfortable handling large datasets stored in AWS using Python Collaborative mindset Passion for research and financial markets Intellectual curiosity, exceptional attention to detail, and the ability to manage multiple projects and deadlines in a fast-paced environment Commitment to the highest ethical standards

Role: Point72 is looking for a Quantitative Developer to join its Fund Flow Research team. The Fund Flow Group provides best in class flow and positioning indicators to help PMs, analysts, and traders across all investment functions understand their investment backdrop, better assess reward and risk, and identify alpha opportunities. Responsibilities : Partner with researchers to release high-quality production code Integrate AI into written content creation, internal client service, and team operations Develop generic frameworks to build flow datasets across equities, credit, and macro Troubleshoot and resolve production issues Requirements: Undergraduate degree or higher in a technical field Strong programming skills in Python Strong orientation towards team success and desire to mentor teammates Passion for technology and markets Strong written and verbal communication skills Intellectual curiosity, exceptional attention to detail, and the ability to manage multiple projects and deadlines in a fast-paced environment Commitment to the highest ethical standards

Please send CVs to [email protected] with “202 7 QR Summer Internship Application” in the subject line. When your application is received, we will consider you for all similar positions at Cubist. About Our Firm: Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. About Our Team: KEPL is a fast-growing team at Cubist Systematic Strategies. We specialize in trading medium-frequency statistical arbitrage strategies with high Sharpe. The team is made up of people from top universities and top tier trading and tech firms. We have an open and collaborative culture, and we value rigorous research and innovative technologies. Role / Experience: We are looking for exceptional students to be our quantitative researcher interns for the summer of 2027 . An ideal candidate should have a strong passion and initiative to work in a start-up environment. He/she should have strong analytical skills and be able to solve hard problems rigorously. Our typical intern candidates come from quantitative PhD programs of top US universities. Our internship program offers unique KEPL experience. During the internship, our interns will receive rigorous and comprehensive trainings. They will develop strong research skills through working closely with our full-time researchers on brand new quant trading models with real-world impact. We will consider full-time offers for interns after the internship. Requirements: PhD candidate in math/physics/statistics/EE/CS, or other quantitative fields Strong knowledge of computational math, probability, and statistics Strong analytical skills, with attention to details Good communication skills Willing to work in a fast-paced start-u

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose. We are accepting applications for this position until 10/16/2026 Position Overview: Are you looking for a company to help prepare you for a successful career? The Multifamily Division within Freddie Mac is searching for talented and creative individuals to support the division’s objectives, projects and initiatives. Freddie Mac Multifamily is a dynamic organization, providing interns a wide variety of experiences and responsibilities to build technical and soft skills, as well as their network. Interns in Freddie Mac Multifamily can actively work on real-world projects and deals, which will provide a broad exposure to Freddie Mac’s important role in the mortgage industry, while developing skills that will apply directly to a career in commercial real estate! Our Impact: Freddie Mac Multifamily helps to ensure an ample supply of affordable rental housing by purchasing mortgages secured by apartment buildings with five or more units We provide mortgage funding for apartment loans throughout the nation, purchasing loans from a network of lenders and then securitizing them Our funding spans the nation, and we cover large metropolitan areas, mid-market cities, and smaller communities, housing a wide array of renters (i.e., students, working families, etc.) Your Impact: Based on your intern role, you will leverage portfolio data and models to assist in the monitoring and assessment of portfolio performance, credit trends. You will assist wi

What you’ll do Partner with medical image reconstruction scientists / engineers to build ML components that improve reconstruction quality, speed, robustness, or quantitative accuracy. Define training/evaluation pipelines, datasets, and metrics that map to user needs and design requirements. Productionize models: inference performance, reproducibility, monitoring for drift/regressions, and safe fallbacks. Collaborate on hybrid algorithms, incorporating physics and learned priors, denoisers, learned regularizers, and quality estimation. Help build tooling for rapid experimentation as well as rigorous verification of algorithm changes. What we’re looking for Strong applied ML experience plus comfort with signal processing / imaging or adjacent domains. Ability to move fluidly between research prototypes and production-quality systems. Strong evaluation discipline: metrics, ablations, data leakage avoidance, and reproducibility. A demonstrated track record of applying ML to physics-based or inverse problems (i.e., shipped projects, a portfolio, or publications.) Useful experience ML for imaging/inverse problems (or adjacent) with strong evaluation discipline and comfort with GPU performance constraints. Pragmatic production mindset: reproducible training/inference, regression testing, and safe deployment in high-stakes contexts. A background in computational physics or scientific computing. Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging. Experience in Agentic-SciML is a plus. Hands-on experience with data curation for ML: building datasets from messy, real-world sources, defining ground truth, and managing labeling or simulation pipelines. Background in data assimilation: combining observations with physics-based models (Kalman filtering, variational methods, ensemble approaches, or learned variants).

Who are we: Graviton is a privately funded quantitative trading firm striving for excellence in financial markets research. We trade across a multitude of asset classes and trading venues using a gamut of concepts and techniques ranging from time series analysis, filtering, classification, stochastic models, pattern recognition, to statistical inference analyzing terabytes of data to come up with ideas to identify pricing anomalies in financial markets. As part of this team you will be tasked to apply machine learning and specifically deep learning techniques to trading problems while staying connected to broader research community. The researcher will put theory into practice and can immediately impact the global trading landscape with the expanding presence of Graviton in various markets. Description Lead research in applying machine learning to a wide variety of datasets and trading problems Follow latest developments in academic research and incorporating research techniques from different fields of applications to our problems Improve tick-by-tick order book based time series feature sets using latest preprocessing techniques Work on current and develop new deep learning models to exploit large pool of in-house features and computing infrastructure Develop scalable pipeline for building predictive models across global markets Discover and implement new sources of predictive alpha, verify that they improve existing models, and integrate them into the firm's strategy development pipeline Partner with quant researchers and software developers in implementation of conducted research to production using Python / C++ Advise infrastructure support team on latest developments on hardware and software to improve computing infrastructure for ML based research Qualifications Masters or PhD in Computer Science, Mathematics, Statistics, or a related field At least two years of demonstrated experience of ML/AI research in a professional setting or at a repu

About Graviton Research Capital: Graviton Research Capital is a cutting-edge high-frequency trading firm that leverages technology, quantitative research and algorithms to trade in global markets. Our team consists of world-class technologists, researchers and traders who collaborate to push the boundaries of automated trading. Role Overview : We’re looking for a Central Execution Trader to join our Singapore team and become a key player on the Central Trading Desk . This team interfaces with, and acts as the communication hub for all brokers and exchanges on behalf of the firm externally, as well as technology, quantitative researchers, risk management, compliance, and business development teams internally. In this role, you’ll be building and refining systems that keep our live trading running flawlessly. From designing real-time monitoring tools to automating critical workflows, you’ll be at the heart of ensuring smooth and efficient trade execution. Key Responsibilities: Own one or more markets and the day-to-day execution and operational health of the live strategies in them. Start strategies at the open, track their state and positions through the session, and close the day with correct positions and files for the next session. Act as the first line of defense for alerts, order rejects and outages in those markets, and as the primary contact for brokers and exchanges on live issues. Diagnose the issue, contain the impact and hand the root cause to the owning quant or technology team. Build and enhance real-time monitoring and safety tooling for live and post-trade environments. Design checks that detect abnormal order flow, position and PnL moves, and surface them to the desk in time to act. Work on live data from the firm's trading systems and from brokers, and take the tooling from idea to production on the desk. Communicate clearly on incidents, resolutions, and process improvements with cross-functional teams, ensuring zero ambiguity. Required Skills

NK Securities Research is a leading financial firm that leverages cutting edge technology and sophisticated algorithms to trade the financial markets. Founded in 2011, we have gained invaluable experience in the field of High Frequency Trading across different asset classes. Responsibilities: As a Quantitative Researcher, you will: • Develop and enhance trading models using our in-house platform • Analyze extensive financial data sets to unearth trading opportunities • Provide analytical support to our experienced traders • Develop predictive models for market movements • Mentor interns and freshers, grooming them as future teammates Qualifications: The ideal candidate will possess: A degree in Computer Science, Mathematics, or Engineering from a leading institution. 0-4 years of relevant work experience. Exceptional analytical and problem-solving skills. Proficiency in programming, particularly in C++ or C. Working knowledge of Linux, Python, and shell scripting. A curious mindset and a passion for understanding complex systems. A disciplined and consistent work ethic. Strong communication and interpersonal skills. Previous experience in a startup or the High-Frequency Trading (HFT) industry will be an added advantage. What We Offer: Competitive salary package Opportunity to work in a dynamic and collaborative environment Career growth and development opportunities Catered breakfast and lunch Annual international and domestic trips Monthly team dinners