quantitative researcher

Monad Foundation

<div><p>The <b>Monad Foundation</b> is a team of dedicated ecosystem and community builders who are on a mission to massively grow the impact of decentralized tech. We believe that the Monad blockchain-the performant and parallel EVM Layer 1-will help decentralized apps eat the world.</p><p></p><h2 class="jobSectionHeader"><b>
The Role</b></h2><p>
We are looking for an exceptional quant to work on data science and machine learning problems in the blockchain space. The work will be challenging, as it will involve predictive modeling for a variety of topics like transaction dependencies, user behavior, network behavior, etc. We&rsquo;re looking for someone who has excelled in other environments where predictive analytics had a direct impact on the bottom line, such as high-frequency trading or traditional tech. Your work will directly impact the performance and economics of one of the biggest upcoming blockchain projects in the space.</p><p></p><h2 class="jobSectionHeader"><b>
What You Will Do</b></h2><ul><li><p>
Work on greenfield predictive modeling problems related to blockchain performance and system/user behavior. Problems may be open-ended; you&rsquo;ll have to devise and prototype a variety of approaches before finding the correct solution.</p></li><li><p>
Implement production-grade solutions and take end-to-end ownership of production prediction pipelines.</p></li></ul><p></p><h2 class="jobSectionHeader"><b>
Who You Are</b></h2><ul><li><p>
At least 3 years of experience building predictive models, preferably at a HFT firm</p></li><li><p>
You&rsquo;re creative, self-motivated and independent</p></li><li><p>
You have excellent knowledge of predictive modeling techniques including linear regression, decision trees, and neural nets</p></li><li><p>
You know numpy and pandas like the back of your hand</p></li><li><p>
You&rsquo;ve built something significant from scratch</p></li><li><p>
Bonus: You are crypto-native</p></li></ul><h2 class="jobSectionHeader"><b>
Why Work with Us</b></h2><ul><li><p><b>
Challenging problems. </b>You&rsquo;ll tackle deeply complex and technically demanding problems, with autonomy and impact.</p></li><li><p><b>
Endless Opportunity for Impact.</b> The Ethereum Virtual Machine (EVM) standard is ubiquitous, but existing EVM-compatible chains are slow and bandwidth-constrained. Monad&rsquo;s core innovations offer developers and founders the best of both worlds (portability and performance) and are a game-changer to power global on-chain finance.</p></li><li><p><b>
The right team.</b> You&rsquo;ll be part of a world class team, who are exceptional and highly-motivated.</p></li><li><p><b>
Culture</b>. We&rsquo;re a lean team working together to achieve very ambitious goals. We are united in our culture of collaboration, low ego, and high-quality output.</p></li><li><p><b>
Strong Ecosystem</b>. The broader Monad ecosystem has attracted support from leading investors, builders and long-term contributors.</p></li></ul></div>

Quantitative Researcher

AKUNA CAPITAL

<div><p>Akuna Capital is an innovative trading firm with a strong focus on collaboration, cutting-edge technology, data driven solutions, and automation. We specialize in providing liquidity as an options market-maker &ndash; meaning we are committed to providing competitive quotes that we are willing to both buy and sell. To do this successfully, we design and implement our own low latency technologies, trading strategies, and mathematical models.</p>
<p>
Our Founding Partners<i> </i>first conceptualized Akuna in their hometown of Sydney. They opened the firm's first office in 2011 in the heart of the derivatives industry and the options capital of the world &ndash; Chicago. Today, Akuna is proud to operate from additional offices in Sydney, Shanghai, London, and Singapore.</p>
<p><b>
What you'll do as a Quantitative Researcher at Akuna:</b></p>
<p>
Akuna's Prediction Markets team is looking to add a Quantitative Researcher focused on sports related contracts. The successful candidate will play a crucial role in developing Akuna's sports pricing capabilities, working closely with development and trading teams to refine and deploy these models alongside our strategies. This position offers the opportunity to make a direct impact on an exciting new desk at Akuna. The ideal candidate will possess strong programming and analytical skills, a creative mindset, and a passion for sports. In this role you will:</p>
<ul>
<li>Design and develop data capture pipelines to enable the pricing of a variety of sports contracts (match outcomes, player props, in-game events)</li>
<li>Develop and calibrate pricing models using historical data in service of in-play quoting of sports contracts.</li>
<li>Build and maintain internal tooling to facilitate all aspects of the research pipeline</li>
<li>Work closely with trading and development to deploy models into production trading strategies, bridging the gap from research to production with an ownership mentality</li>
</ul>
<p><b>Qualities that make great candidates:</b></p>
<ul>
<li>Bachelor's, Master's, or PhD degree in a technical field, such as Engineering, Computer Science, Mathematics, or Physics (or a related subject)</li>
<li>Proficient programming skills in Python (C++ is a plus)</li>
<li>2+ years of experience in a quantitative or programming role, preferably within the trading sector.</li>
<li>Deep understanding of fields such as Linear Algebra, Numerical Methods, Statistics, Optimization, and Machine Learning</li>
<li>Strong interest in sports and quantitative modeling.</li>
</ul>
<p>We are especially interested in high agency individuals who have practical experience in financial markets and see the recent rise in prediction markets as an opportunity to ply their trade to a new domain they find more dynamic and exciting: sports. If you are a working quant and a sports data nerd, we want to hear from you!</p>
<p><b><i>
In addition to technical skillsets, Akuna values the unique perspectives people can bring to the table to collaboratively solve complex problems and drive Akuna forward. We want everyone to feel empowered to apply. We welcome your application and encourage you to take the first steps toward your future with us!</i></b></p>
<p><i>
In accordance with Illinois Equal Pay Act, the minimum base salary starts at $145,000. Exact compensation offered may vary based on many factors including, but not limited to, the candidate's experience, qualifications, and skill set. This role is also eligible for a discretionary performance bonus as part of the total compensation package and includes a comprehensive benefits package that may encompass employer-paid medical, dental, vision, retirement contributions, paid time off, and other benefits. The minimum base salary herein was determined in good faith by Akuna Capital LLC.</i></p></div>

Quantitative Researcher - Prediction Markets

JPMorganChase

<div><b>JOB DESCRIPTION</b><div><br>
</div><div>Join our global Quantitative Trading &amp; Research (QTR) team, where you'll apply your expertise in Derivatives Modelling, Financial Engineering, Data Science, and Quantitative Development. As part of JP Morgan's leading QR Group, you'll innovate with unique analytics and mathematical models, enhancing business practices through automation. We develop advanced models and methodologies to support the Clearing business, utilizing the Athena quant platform for comprehensive trade and risk management across all asset classes.</div><div>
</div><div><b>Job summary:</b></div><div>
</div><div>As an Associate Quantitative Researcher in the Quantitative Trading &amp; Research (QTR) Global Clearing team, you will lead the design, delivery, and governance of risk and pricing analytics and models across F&amp;O and OTC derivatives, with a primary focus on risk analytics, Initial Margin (IM) methodology, and production execution. You will set technical direction and partner closely with the Margin Trading desk, Technology, and Product Development to ship high-impact solutions. You will also shape our data-led strategy by applying state-of-the-art machine learning to transform risk management and automation across the investment bank.</div><div>
</div><div><b>Job responsibilities</b></div><ul><li>
Own delivery of front-office risk/pricing analytics and margin solutions using internal derivatives libraries, ensuring robust, performant outcomes across D1, F&amp;O, and OTC products; define multi-quarter roadmaps and drive continuous improvement.</li><li>
Lead end-to-end initiatives&mdash;from problem framing and hypothesis design through prototyping, backtesting, and scalable production deployment&mdash;partnering with Trading, QR peers, Technology, and Product to deliver measurable business impact.</li><li>
Design and enhance margin and derivative models, including methodology selection, calibration, numerical schemes, benchmarking/backtesting, documentation, and alignment with model risk governance.</li><li>
Serve as model owner: manage roadmaps, controls, monitoring/alerts, change management, and responses to Model Risk, Audit, and regulatory reviews; ensure explainability and transparency of assumptions, limitations, and model performance.</li><li>
Build and productionize analytics that advance intraday/EoD automation (services, APIs, pipelines) with clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms.</li><li>
Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best practices (testing, CI/CD, reproducibility), and develop team capability.</li><li>
Communicate complex quantitative concepts to non-technical audiences; influence product roadmaps, prioritization, and resourcing via data-driven analysis, scenario studies, and clear articulation of trade-offs.</li><li>
Lead development of ML/AI solutions end-to-end (feature engineering, model training/validation, MLOps, monitoring and drift management) with rigorous controls, documented governance, and demonstrable business value.</li><li>
Uphold standards for documentation, reproducibility, traceability, and SDLC; ensure compliance with internal policies for model risk management and data governance.</li></ul><div>
</div><div><b>Required qualifications, capabilities, and skills</b></div><ul><li>
Advanced degree (PhD, MSc, or equivalent) in Mathematics, Physics, Statistics, Computer Science, or a related quantitative field.</li><li>
5+ years of front-office quant experience supporting trading/risk in F&amp;O and/or OTC derivatives, with a track record of production delivery and close trader partnership.</li><li>
Deep knowledge of listed and OTC derivatives; strong understanding of risk/P&amp;L attribution, sensitivities/Greeks, model assumptions/limitations, and market microstructure.</li><li>
Experience with front-office platforms such as SecDB, Athena, Quartz, or equivalent.</li><li>
Strong programming in Python and/or C++; experience architecting maintainable, testable, high-performance codebases and extending large-scale libraries; proficiency in numerical methods and performance tuning.</li><li>
Proven experience designing, calibrating, and maintaining IM/pricing models (e.g., curve construction, volatility surfaces, credit/rates models, margin frameworks), including performance monitoring and backtesting.</li><li>
Experience delivering production services with Technology partners (APIs, packaging, CI/CD, containerization, logging/monitoring); familiarity with data engineering and compute frameworks.</li><li>
Excellent quantitative problem-solving; able to decompose ambiguous problems, select appropriate methods, and communicate uncertainty and trade-offs clearly.</li><li>
Outstanding communication and stakeholder management; ability to influence across QR, Trading, Technology, and Product.</li><li>
Demonstrated mentorship or team leadership experience, including setting technical direction, conducting reviews, and managing priorities under pressure.</li></ul><div>
</div><div><b>Preferred qualifications, capabilities, and skills</b></div><ul><li>
Expertise in curve building (multi-curve frameworks), volatility surface modeling/calibration (e.g., SABR, Heston, local/stochastic volatility), and numerical methods (PDE/FDM, Monte Carlo, adjoint/automatic differentiation).</li><li>
Experience with market risk, time-series/stress analytics, model risk governance, and regulatory expectations for pricing/risk models.</li><li>
Hands-on ML/AI for quant finance (signal extraction, surrogate modeling, anomaly detection), including MLOps, drift monitoring, and explainability.</li><li>
Knowledge of portfolio optimization, hedging algorithms, execution analytics, and transaction cost modeling.</li><li>
Familiarity with distributed computing and market data tooling (e.g., kdb+/q, SQL), and performance engineering for large-scale simulations.</li><li>
Contributions to research (internal notes, publications, patents, conference talks) and engagement with the open-source scientific computing ecosystem.</li></ul><p>
</p><b>ABOUT US</b><br>
<div>
<div>JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world&rsquo;s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
</div><p></p><div><p>We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
</p><div><p>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants&rsquo; and employees&rsquo; religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.</p>
<p>JPMorgan Chase &amp; Co. is an Equal Opportunity Employer, including Disability/Veterans</p><br>
</div></div></div><br>
<br>
<b>ABOUT THE TEAM</b><br>
<br>
J.P. Morgan&rsquo;s Commercial &amp; Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial &amp; Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.</div>

Quantitative Trading & Research - Global Clearing - Associate

<div><b>JOB DESCRIPTION</b><p><br>
Join our global Quantitative Trading &amp; Research (QTR) team, where you'll apply your expertise in Derivatives Modelling, Financial Engineering, Data Science, and Quantitative Development. As part of JP Morgan's leading QTR Group, you'll innovate with unique analytics and mathematical models, enhancing business practices through automation. We develop advanced models and methodologies to support the Clearing business, utilizing the Athena quant platform for comprehensive trade and risk management across all asset classes.</p><p>
<b>Job summary:</b></p><p>
As a Vice President Quantitative Researcher in the Quantitative Trading &amp; Research (QTR) Global Clearing team, you will lead the design, delivery, and governance of risk and pricing analytics and models across F&amp;O and OTC derivatives, with a primary focus on risk analytics, Initial Margin (IM) methodology, and production execution. You will set technical direction and partner closely with the Margin Trading desk, Technology, and Product Development to ship high-impact solutions. You will also shape our data-led strategy by applying state-of-the-art machine learning to transform risk management and automation across the investment bank.</p><p>
<b>Job responsibilities</b></p><ul><li>
Own delivery of front-office risk/pricing analytics and margin solutions using internal derivatives libraries, ensuring robust, performant outcomes across D1, F&amp;O, and OTC products; define multi-quarter roadmaps and drive continuous improvement.</li><li>
Lead end-to-end initiatives&mdash;from problem framing and hypothesis design through prototyping, backtesting, and scalable production deployment&mdash;partnering with Trading, QR peers, Technology, and Product to deliver measurable business impact.</li><li>
Design and enhance margin and derivative models, including methodology selection, calibration, numerical schemes, benchmarking/backtesting, documentation, and alignment with model risk governance.</li><li>
Serve as model owner: manage roadmaps, controls, monitoring/alerts, change management, and responses to Model Risk, Audit, and regulatory reviews; ensure explainability and transparency of assumptions, limitations, and model performance.</li><li>
Build and productionize analytics that advance intraday/EoD automation (services, APIs, pipelines) with clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms.</li><li>
Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best practices (testing, CI/CD, reproducibility), and develop team capability.</li><li>
Communicate complex quantitative concepts to non-technical audiences; influence product roadmaps, prioritization, and resourcing via data-driven analysis, scenario studies, and clear articulation of trade-offs.</li><li>
Lead development of ML/AI solutions end-to-end (feature engineering, model training/validation, MLOps, monitoring and drift management) with rigorous controls, documented governance, and demonstrable business value.</li><li>
Uphold standards for documentation, reproducibility, traceability, and SDLC; ensure compliance with internal policies for model risk management and data governance.</li></ul><p>
</p><p><b>Required qualifications, capabilities, and skills</b></p><ul><li>
Advanced degree (PhD, MSc, or equivalent) in Mathematics, Physics, Statistics, Computer Science, or a related quantitative field.</li><li>
5+ years of front-office quant experience supporting trading/risk in F&amp;O and/or OTC derivatives, with a track record of production delivery and close trader partnership.</li><li>
Deep knowledge of listed and OTC derivatives; strong understanding of risk/P&amp;L attribution, sensitivities/Greeks, model assumptions/limitations, and market microstructure.</li><li>
Experience with front-office platforms such as SecDB, Athena, Quartz, or equivalent.</li><li>
Strong programming in Python and/or C++; experience architecting maintainable, testable, high-performance codebases and extending large-scale libraries; proficiency in numerical methods and performance tuning.</li><li>
Proven experience designing, calibrating, and maintaining IM/pricing models (e.g., curve construction, volatility surfaces, credit/rates models, margin frameworks), including performance monitoring and backtesting.</li><li>
Experience delivering production services with Technology partners (APIs, packaging, CI/CD, containerization, logging/monitoring); familiarity with data engineering and compute frameworks.</li><li>
Excellent quantitative problem-solving; able to decompose ambiguous problems, select appropriate methods, and communicate uncertainty and trade-offs clearly.</li><li>
Outstanding communication and stakeholder management; ability to influence across QR, Trading, Technology, and Product.</li><li>
Demonstrated mentorship or team leadership experience, including setting technical direction, conducting reviews, and managing priorities under pressure.</li></ul><p>
</p><p><b>Preferred qualifications, capabilities, and skills</b></p><ul><li>
Expertise in curve building (multi-curve frameworks), volatility surface modeling/calibration (e.g., SABR, Heston, local/stochastic volatility), and numerical methods (PDE/FDM, Monte Carlo, adjoint/automatic differentiation).</li><li>
Experience with market risk, time-series/stress analytics, model risk governance, and regulatory expectations for pricing/risk models.</li><li>
Hands-on ML/AI for quant finance (signal extraction, surrogate modeling, anomaly detection), including MLOps, drift monitoring, and explainability.</li><li>
Knowledge of portfolio optimization, hedging algorithms, execution analytics, and transaction cost modeling.</li><li>
Familiarity with distributed computing and market data tooling (e.g., kdb+/q, SQL), and performance engineering for large-scale simulations.</li><li>
Contributions to research (internal notes, publications, patents, conference talks) and engagement with the open-source scientific computing ecosystem.</li></ul><p>
</p><p></p><b>ABOUT US</b><br>
<div>
<div>JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world&rsquo;s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
</div><p></p><div><p>We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
</p><div><p>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants&rsquo; and employees&rsquo; religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.</p>
<p>JPMorgan Chase &amp; Co. is an Equal Opportunity Employer, including Disability/Veterans</p><br>
</div></div></div><br>
<br>
<b>ABOUT THE TEAM</b><br>
<br>
J.P. Morgan&rsquo;s Commercial &amp; Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial &amp; Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.</div>

Quantitative Trading & Research – Global Clearing – Vice President

Moreton Capital Partners

<div>
 <strong>
  Immediate Start - Machine Learning Researcher (PhD) - Systematic Commodities Hedge Fund
  <br/>
  <br/>
 </strong>
 Moreton Capital Partners is rapidly expanding and seeking a talented Machine Learning Researcher to help design and improve the predictive models that power our systematic commodities trading strategies in our Mexico City Office.
 <br/>
 <br/>
 We trade global commodity futures using machine learning, alternative data, and institutional-grade portfolio construction. Our edge comes from research depth, disciplined experimentation, and robust production systems.
 <br/>
 <br/>
 This role is for candidates completing or having recently completed a PhD with a strong machine learning, statistics, or applied mathematics focus who want to apply advanced research in a real capital environment. You will work directly with the CIO and sit alongside a world class international quant research team to turn cutting-edge ML ideas into live trading signals. Your research will ship to production and directly impact portfolio returns.
 <br/>
 <br/>
 This is not a purely academic role. We're looking for someone ready to hit the ground running and available to start immediately. In return, we offer a competitive salary, substantial performance share, comprehensive benefits, incredible work environment and a relocation package to make the move seamless.
 <br/>
 <br/>
 <strong>
  What You Will Work On
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Designing predictive models for cross-sectional and time-series commodity returns
  </li>
  <li>
   Developing new features from price, positioning, options, macro, and alternative datasets
  </li>
  <li>
   Improving signal robustness and reducing overfitting through rigorous validation
  </li>
  <li>
   Combining and blending multiple models into portfolio-level forecasts
  </li>
  <li>
   Regime detection, meta-models, and adaptive allocation frameworks
  </li>
  <li>
   Model diagnostics, explainability, and stability analysis
  </li>
  <li>
   Translating research ideas into production-ready implementations
  </li>
  <li>
   Collaborating with engineers to deploy models into live trading systems
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Key Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Formulate research hypotheses and test them using clean, time-aware ML pipelines
  </li>
  <li>
   Build and evaluate models (tree-based, linear, ensemble, deep learning, etc.)
  </li>
  <li>
   Run walk-forward and out-of-sample experiments with realistic costs
  </li>
  <li>
   Analyze information coefficients, turnover, drawdowns, and risk-adjusted returns
  </li>
  <li>
   Design feature engineering frameworks and reusable research tooling
  </li>
  <li>
   Document findings clearly and communicate results to portfolio managers
  </li>
  <li>
   Contribute to improving research standards, reproducibility, and processes
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Requirements
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   PhD (completed or near completion) in Machine Learning, Statistics, Applied Mathematics, Computer Science, Physics, Engineering, or related quantitative field
  </li>
  <li>
   Strong Python skills and experience with scientific computing stacks
  </li>
  <li>
   Deep understanding of statistical learning and model validation
  </li>
  <li>
   Experience working with large datasets and experimental pipelines
  </li>
  <li>
   Ability to move from theory to practical implementation
  </li>
  <li>
   Intellectual curiosity and strong problem-solving mindset
  </li>
  <li>
   Comfortable working in a fast-paced, high-ownership environment
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Bonus Points For
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience with financial markets or systematic trading
  </li>
  <li>
   Familiarity with time-series modelling or forecasting
  </li>
  <li>
   Experience with LightGBM/XGBoost, deep learning, or ensemble methods
  </li>
  <li>
   Exposure to portfolio construction or risk modelling
  </li>
  <li>
   Experience with cloud or distributed compute environments
  </li>
  <li>
   Published research or strong applied projects
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Why this role is unique
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Direct impact: your research drives live trading capital
  </li>
  <li>
   Research freedom: explore ideas with fast feedback loops
  </li>
  <li>
   Real-world data: large, messy, multi-source datasets
  </li>
  <li>
   Small team: high ownership and rapid iteration
  </li>
  <li>
   Strong learning curve across ML, markets, and portfolio construction
  </li>
  <li>
   Clear path into Senior Researcher or Portfolio Manager responsibilities
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Benefits
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Market leading benefits
  </li>
  <li>
   High responsibility from day one
  </li>
  <li>
   Attractive compensation: Highly competitive base salary and annual bonus that scales as the business grows
  </li>
  <li>
   Relocation package to our Mexico City office, along with a competitive benefits offering that includes health and life insurance, a year-end bonus, and generous paid time off
  </li>
  <li>
   Positive, inclusive and encouraging work environment
  </li>
  <li>
   Close collaboration across a global team
  </li>
 </ul>
</div>


Machine Learning Researcher (PhD) - Systematic Commodities Hedge Fund

Bloomberg

<div><div><div><div><div><div>Quant Researcher - Agentic AI CTO Office
</div></div></div></div></div><div><div><div><div><div>Location
</div><div>New York
</div></div><div><div>Business Area
</div><div>Engineering and CTO
</div></div><div><div>Ref #
</div><div>10050703
</div></div></div></div></div><div><div><div><h2 class="jobSectionHeader"><b>Description &amp; Requirements
</b></h2></div></div><div><div><div><div><div><b>Who we are:</b></div>
<div>The Bloomberg CTO Office is the future-looking technical arm of Bloomberg L.P. We envision, design and prototype the next generation infrastructure, hardware and applications that interface in all aspects of the company including financial products, broadcast and media, data centers, internal IT and our global network. We are passionate about what we do.
</div><div><br>
<b>What we do:
</b></div><div>BQuant is Bloomberg&rsquo;s cloud-hosted quantitative investment research platform, built on JupyterLab and designed specifically for financial markets.
</div><div><br>
As the centralized BQuant Research team, we drive product innovation through cutting-edge research in Agentic AI, Machine Learning, NLP, and Quantitative Finance. We work across asset classes and market domains to prototype next-generation solutions for buy-side and sell-side institutions (including hedge funds, asset managers, and investment banks). Our mission is to help clients accelerate their research, portfolio decisions, and trade execution by leveraging the unique combination of Bloomberg&rsquo;s financial datasets, advanced AI technology, and domain expertise.
</div><div><br>
We work at the forefront of AI in capital markets, tackling challenges in equity, fixed income, macro, and multi-asset workflows. Our team operates in a highly collaborative environment, engaging with product managers, engineers, UX designers, and sales to deliver transformative client solutions.
</div><div><br>
<b>What&rsquo;s in it for you:
</b></div><div>Bloomberg offers the richest and most comprehensive financial datasets across asset classes in the world, paired with extensive product offerings from data to analytics to trading and investment management.
</div><div><br>
You will have the opportunity to:
</div><div><ul><li><b>Build Agentic AI workflow solutions</b> that redefine how the financial industry conducts research, trading, and risk management.
</li><li>Apply <b>advanced ML</b> techniques in an extremely rich problem space with high impact potential across hedge funds, asset managers, and investment banks.
</li><li>Own the full lifecycle of innovation: ideation, prototyping, validation with clients (including top-tier buy-side and sell-side firms), and delivery to production in collaboration with engineering teams.
</li><li>Shape how Bloomberg clients use AI to evolve from research to production in scalable, efficient, and reliable ways.
</li></ul></div><div><br>
The problems you&rsquo;ll work on are complex, cutting-edge, and highly impactful; spanning everything from signal research, to portfolio construction, and to macroeconomic modeling and cross-asset investment strategies.
</div><div><br>
<b>You&rsquo;ll need to have:
</b></div><div><ul><li>10+ years in quantitative research, machine learning, or financial AI development, including <b>5+ years in capital markets (equity, fixed income, or macro)
</b></li><li>Prior experience designing and deploying <b>AI solutions </b>in a financial market context
</li><li>Advanced degree (Masters/PhD) in a quantitative field (e.g., Computer Science, Financial Engineering, Statistics, Applied Math, Physics)
</li><li>Strong foundation in statistics, ML, and AI, with proven real-world applications in finance
</li><li>Familiarity with AI tools for coding and innovation process
</li><li>Excellent collaboration skills with quants, researchers, and engineers
</li><li>Strong communication skills to engage internal stakeholders and external clients
</li></ul></div><div><br>
<b>We&rsquo;d like to see:
</b></div><div><ul><li>Research experience in multi-asset and cross-asset strategies
</li><li>Deep curiosity and passion for<b> Agentic AI solutions</b> in finance
</li>A track record of overcoming complex challenges through collaboration and innovation<br>
<li>
</li></ul></div></div></div><div></div><br>
<div><div><div>Salary Range = 155000 - 285000 USD Annually + Benefits + Bonus</div><div><br>
<div><br>
</div><div>The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.<div><br>
<br>
We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.<br>
</div></div></div></div></div><div><div>Discover what makes Bloomberg unique - watch our for an inside look at our culture, values, and the people behind our success.</div></div></div></div></div></div>

Quant Researcher - Agentic AI CTO Office

Origin Harbor Capital Solutions

<div><h1 class="jobSectionHeader"><b>Summary</b></h1><div><p>
 The Multi-Asset Solutions Team (MAST) manages a suite of active investment ETFs and delivers standard and customized multi-asset portfolio solutions across approximately $5 billion in AUM. Portfolios span U.S. and international equities, fixed income, and commodities. MAST&rsquo;s investment process combines systematic quantitative models with a qualitative investment overlay. The Quantitative Research function is central to this process, designing, maintaining, and enhancing the models that translate market and macroeconomic information into portfolio allocations. These include:</p></div><div><ul><li>
Market regime and business-cycle detection models</li></ul></div><div><ul><li>
State-space and signal-aggregation frameworks</li></ul></div><div><ul><li>
Bespoke portfolio optimization engines</li></ul></div><div><ul><li>
Scenario analysis and Monte Carlo simulations for outcome evaluation</li></ul><p><br>
</p></div><div><p>The Senior Quantitative Analyst is a hands-on role spanning research, model development, portfolio construction, and production. We are looking for a seasoned quantitative professional who has built trading signals, constructed portfolios, and deployed models that run real money. This person will work closely with portfolio managers and trade operations, and is expected to elevate the team&rsquo;s research capability through deep technical expertise and hard-won market intuition.
</p></div><p></p><h1 class="jobSectionHeader"><b>Key </b><b>Responsibilities</b></h1><div><div><ul><li>
Design, develop, and maintain systematic investment models &mdash; including regime detection, business-cycle indicators, signal aggregation frameworks, and multi-asset portfolio optimization engines &mdash; that directly drive portfolio allocation decisions across approximately $5B in AUM.</li></ul></div><div><ul><li>
Bring deep technical expertise in classical quantitative methods &mdash; mean-variance and robust optimization, state-space models, Kalman filters, factor models, time-series econometrics, and Monte Carlo simulation. You should understand these tools at a foundational level, not just as library calls.</li></ul></div></div><div><div><ul><li>
Complement that classical foundation with practical machine learning and AI experience &mdash; ensemble methods, gradient boosting, reinforcement learning, deep sequence models, LLM-based pipelines, and dimensionality reduction techniques. The team actively integrates AI into its research and production workflows. More importantly, you should know when these approaches add genuine value over simpler methods and when they don&rsquo;t.</li></ul></div><div><ul><li>
Bring a proven track record of taking models from research through to live production. We expect candidates at this level to have done this multiple times &mdash; designed a model, validated it through rigorous backtesting, deployed it into a production investment process, and maintained it through changing market regimes.</li></ul></div><div><ul><li>
Conduct independent, self-directed quantitative research to identify new signals, improve existing models, and advance the team&rsquo;s investment process &mdash; formulating hypotheses, sourcing and validating data, running rigorous backtests, and presenting actionable findings to portfolio managers.</li></ul></div><div><ul><li>
Bridge the gap between quantitative methods and investment judgment. Translate complex model outputs into clear, portfolio-relevant insights for PMs and stakeholders, bringing a cross-asset perspective informed by deep understanding of macro drivers, market regimes, and multi-asset dynamics.</li></ul></div><div><ul><li>
Raise the intellectual bar of the team. Draw on your experience across markets, methodologies, and past failures to challenge assumptions, introduce new frameworks, and sharpen the team&rsquo;s research process. We value people who make everyone around them better by sharing hard-won intuition about what works, what doesn&rsquo;t, and why.</li></ul></div><div><ul><li>
Write production-quality Python code every day. This is a hands-on role: every member of the team builds and owns their own code.</li></ul><p></p><div><p>
Research &amp; Model Development</p></div><div><ul><li>
Research and develop quantitative methods for asset allocation, regime modeling, and portfolio optimization.</li></ul></div><div><ul><li>
Enhance existing models and analytics through data, methodology, or implementation improvements.</li></ul></div><div><ul><li>
Perform back-testing, scenario analysis, Monte Carlo simulations and sensitivity studies.</li></ul></div><div><ul><li>
Conduct quantitative analyses to drive investment decisions.</li></ul></div><p></p><div><p><br>
Portfolio Construction &amp; Design</p></div><div><ul><li>
Construct and design optimized portfolios and explain key drivers of allocations and design decisions.</li></ul></div><div><ul><li>
Assess model output, diagnose when a model will/will not work, apply overrides and propose gates/enhancements.</li></ul></div><div><ul><li>
Review and assess consistency, accuracy and robustness of MAST models and processes.</li></ul><p><br>
</p></div></div></div><h1 class="jobSectionHeader"><b>Key Behavioral Expectations</b></h1><p>
Drives for Results</p><ul><li>
Demonstrates ownership, accountability, and urgency.</li><li>
Delivers high-quality output aligned with Harbor&rsquo;s investment objectives.</li></ul><p></p><p>
Unleashes Innovation</p><ul><li>
Actively explores new ideas, techniques, and data sources.</li><li>
Challenges existing approaches while maintaining discipline and rigor.</li></ul><p></p><p>
Communication &amp; Engagement</p><ul><li>
Clearly articulates complex ideas and recommendations.</li><li>
Engages effectively with PMs, peers, and cross-functional partners.</li></ul><p></p><h1 class="jobSectionHeader"><b>
Minimum Qualifications
</b></h1><div><ul><li>Bachelor&rsquo;s degree in a quantitative discipline (e.g., mathematics, statistics, physics, computer science, engineering, economics, finance); advanced degree (Master&rsquo;s or PhD) strongly preferred.</li></ul></div><div><ul><li>
Production-level Python proficiency required. Experience building and maintaining quantitative research systems, optimization engines, and data pipelines in Python is expected, not optional.</li></ul></div><div><ul><li>
The ideal candidate combines deep quantitative expertise with substantive capital markets knowledge. At this seniority, we expect both &mdash; not one with the aspiration to learn the other.</li></ul></div><div><ul><li>
7&ndash;15 years of relevant experience in quantitative investment research, systematic portfolio construction, or a closely related role. Candidates must have a track record of building models that have been deployed in live investment processes managing real capital.</li></ul></div><div>
CFA charterholder or progress toward the designation is a plus, but demonstrated investment judgment through a live track record is valued more highly than credentials.<br>
<ul><li>
</li></ul></div><p></p><h1 class="jobSectionHeader"><b>Knowledge, Skills, &amp; Abilities Required
</b></h1><p>The ideal candidate is intellectually curious, outcome-driven, and brings the judgment that only comes from years of building and running live investment models. They have strong opinions about methodology &mdash; formed through experience, not just reading &mdash; and are comfortable working across multiple initiatives in a fast-paced, small-team investment environment.</p><div><ul><li>
Deep analytical and quantitative reasoning skills with the ability to evaluate model assumptions critically.</li></ul></div><div><ul><li>
Demonstrated experience with advanced statistical methods, machine learning, and AI &mdash; including time-series modeling, state-space methods, reinforcement learning, and ensemble techniques &mdash; applied in a live investment context.</li></ul></div><div><ul><li>
Proven ability to translate quantitative output into investment-relevant insights and defend model-driven recommendations to portfolio managers.</li></ul></div><div><ul><li>
Comfortable leading research workstreams independently while collaborating closely in a small, high-autonomy investment team.</li></ul></div><p></p><h1 class="jobSectionHeader"><b>
Preferred Skills</b><b>
</b></h1><div><ul><li>Extensive experience with large, complex financial and macroeconomic datasets, including Bloomberg, alternative data, and cross-asset market data.</li></ul></div><div><ul><li>
Hands-on experience with portfolio analytics, factor models, optimization under constraints, and back-testing frameworks in a live investment context.</li></ul></div><div><ul><li>
Experience with relational databases (e.g., PostgreSQL, SQL Server) and building production data pipelines.</li></ul></div><div><ul><li>
Track record of building production-quality research tools, APIs, and systematic investment infrastructure.<b>
</b></li></ul></div><p></p><p><b>Compensation Pay Range: </b>This position offers a competitive base salary range of $210,000&ndash;$285,000, commensurate with experience and qualifications.</p></div>

Quantitative Analyst, Portfolio Manager

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