Join Cubist to help build a new systematic macro business focused on mid-frequency alpha strategies across futures, FX, and volatility. You will develop and implement systematic trading models, perform alpha research and backtesting, evaluate new datasets, and contribute to production/trading infrastructure and execution monitoring. The role requires strong quantitative skills, experience with data exploration and feature engineering, and proficiency in Python and the ML stack. It is an early-career research role within a quantitative investing team.
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
Quantitative researcher to help build out a new systematic macro (futures, FX, and vol) business. Core focus will be working on mid-frequency alpha strategies.
Job Description
Develop systematic trading models across FX, commodities, fixed income, and equity markets
Alpha idea generation, backtesting, and implementation
Assist in building, maintenance, and continual improvement of production and trading environments
Evaluate new datasets for alpha potential
Improve existing strategies and portfolio optimization
Execution monitoring
Be a core contributor to growing the investment process and research infrastructure of the team
Desirable Candidates
Masters or PhD in mathematics, statistics, physics or other quantitative discipline. PhD in statistics or machine learning is a plus
Experience in quantitative trading, ideally in FX or futures
Experience with alpha research, portfolio construction and optimization
Experience building statistical/technical, fundamental, and data driven signals
Experience synthesizing predictive signals for both cross-sectional and time-series models
Strong experience with data exploration, dimension reduction, and feature engineering
Thorough understanding of and comfort using a variety of regression techniques—including OLS, MLS, Ridge, Lasso, and Bayesian inference—as well as techniques for dealing with errors that can occur, such as auto-correlation and heteroskedasticity
Experience managing and running risk is a strong plus
Proficiency in Python using the machine learning stack—numpy, pandas, scikit-learn, etc.
Creative mindset
Strong time management ability—the ability to manage multiple tasks and deadlines in a fast-paced environment
High degree of drive and energy—must be a self-starter
Ability to work cooperatively with all levels of staff and to thrive in a team-oriented environment
Commitment to the highest ethical standards and who act with professionalism and integrity at all times
The annual base salary range for this role is $150,000-$200,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
// Mixpanel ignore tracking for known IPs var excludeIPS = '[65.213.72.30, 185.142.16.9, 203.176.115.9, 208.68.197.6, 208.68.197.9, 208.68.199.6, 208.68.199.9, 208.85.160.9, 208.85.161.9]'; $.getJSON('//api.ipify.org?format=json', function(data) { try{ if(true && excludeIPS.indexOf(data.ip) > -1){ console.log('Mixpanel ignore events set: NO events tracked.'); mixpanel.register({"$ignore":true}); }else{ mixpanel.unregister("$ignore"); mixpanel.track("View Page", { "Page Name": document.querySelector('.dotted-underline') ? document.querySelector('.dotted-underline').innerText : location.href, "Careers Site": true }); mixpanel.people.set_once({ 'First Career Page Visit' : new Date().toISOString() }); mixpanel.people.set({ 'Last Career Page Visit' : new Date().toISOString() }); // Only for Careers Site mixpanel.people.increment("# of Career Page Visits"); (function(){ var links = document.querySelectorAll('a'); [].forEach.call(links, function(link) { link.addEventListener("click", function (e) { mixpanel.track("Click Link", { "Link Name": link.text, "Link Location": link.getAttribute('link-location') == null ? 'Body' : link.getAttribute('link-location'), "Link Type": link.getAttribute('link-type') == null ? '' : link.getAttribute('link-type'), "Link Destination URL" : link.href }); }); }); })(); } }catch(e){} }); jQuery(document).ready(function(){ var str = navigator.userAgent; if (str.toLowerCase().indexOf("firefox") >= 0) { jQuery('body').addClass("gecko"); } }); {"employmentType":"FULL_TIME","identifier":{"name":"Cubist Systematic Strategies, LLC","@type":"PropertyValue"},"jobLocation":[{"address":{"addressCountry":"US","addressRegion":"New York","addressLocality":"New York","@type":"PostalAddress"},"@type":"Place"}],"hiringOrganization":{"sameAs":"https://www.point72.com/","name":"Cubist","@type":"Organization"},"datePosted":"2026-01-19","description":"
About Cubist
\n
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
\n
Quantitative researcher to help build out a new systematic macro (futures, FX, and vol) business. Core focus will be working on mid-frequency alpha strategies.
Job Description
\n
Develop systematic trading models across FX, commodities, fixed income, and equity markets
Alpha idea generation, backtesting, and implementation
Assist in building, maintenance, and continual improvement of production and trading environments
Evaluate new datasets for alpha potential
Improve existing strategies and portfolio optimization
Execution monitoring
Be a core contributor to growing the investment process and research infrastructure of the team
Desirable Candidates
\n
Masters or PhD in mathematics, statistics, physics or other quantitative discipline. PhD in statistics or machine learning is a plus
Experience in quantitative trading, ideally in FX or futures
Experience with alpha research, portfolio construction and optimization
Experience building statistical/technical, fundamental, and data driven signals
Experience synthesizing predictive signals for both cross-sectional and time-series models
Strong experience with data exploration, dimension reduction, and feature engineering
Thorough understanding of and comfort using a variety of regression techniques—including OLS, MLS, Ridge, Lasso, and Bayesian inference—as well as techniques for dealing with errors that can occur, such as auto-correlation and heteroskedasticity
Experience managing and running risk is a strong plus
Proficiency in Python using the machine learning stack—numpy, pandas, scikit-learn, etc.
Creative mindset
Strong time management ability—the ability to manage multiple tasks and deadlines in a fast-paced environment
High degree of drive and energy—must be a self-starter
Ability to work cooperatively with all levels of staff and to thrive in a team-oriented environment
Commitment to the highest ethical standards and who act with professionalism and integrity at all times
The annual base salary range for this role is $150,000-$200,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
","title":"Quantitative Researcher - Macro","@type":"JobPosting","@context":"http://schema.org/"} CSJobDetailModule.init('{\"lastModifiedDateFormatted\":\"2026-01-07\",\"job\":{\"attributes\":{\"type\":\"Job__c\",\"url\":\"/services/data/v65.0/sobjects/Job__c/a035b00001HcPQaAAN\"},\"Id\":\"a035b00001HcPQaAAN\",\"Name\":\"Quantitative Researcher - Macro\",\"Assigned_Internal_Recruiter__c\":\"005j000000EWCJ4AAP\",\"Job_Code__c\":\"CSS-0008107\",\"Experience__c\":\"Early Career\",\"Company__c\":\"001j000000VbgA3AAJ\",\"Posted_Location__c\":\"New York\",\"Area__c\":\"Investing\",\"Team__c\":\"Systematic Investing\",\"Summary__c\":\"Senior quantitative researcher to help build out a new systematic macro (futures, FX, and vol) business. Core focus will be working on mid-frequency alpha strategies.\",\"Job_Description_External__c\":\"\u003Ch3\u003EAbout Cubist\u003C/h3\u003E\\n\u003Cp\u003ECubist 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.\u003C/p\u003E\u003Cbr\u003E\u003Ch3\u003ERole\u003C/h3\u003E\\n\u003Cp\u003EQuantitative researcher to help build out a new systematic macro (futures, FX, and vol) business. Core focus will be working on mid-frequency alpha strategies.\u003C/p\u003E\u003Cbr\u003E\u003Ch3\u003EJob Description\u003C/h3\u003E\\n\u003Cul\u003E\u003Cli\u003EDevelop systematic trading models across FX, commodities, fixed income, and equity markets\u003C/li\u003E\u003Cli\u003EAlpha idea generation, backtesting, and implementation\u003C/li\u003E\u003Cli\u003EAssist in building, maintenance, and continual improvement of production and trading environments\u003C/li\u003E\u003Cli\u003EEvaluate new datasets for alpha potential\u003C/li\u003E\u003Cli\u003EImprove existing strategies and portfolio optimization\u003C/li\u003E\u003Cli\u003EExecution monitoring\u003C/li\u003E\u003Cli\u003EBe a core contributor to growing the investment process and research infrastructure of the team\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003E\u003Cbr\u003E\u003C/p\u003E\u003Cbr\u003E\u003Ch3\u003EDesirable Candidates\u003C/h3\u003E\\n\u003Cul\u003E\u003Cli\u003EMasters or PhD in mathematics, statistics, physics or other quantitative discipline. PhD in statistics or machine learning is a plus\u003C/li\u003E\u003Cli\u003EExperience in quantitative trading, ideally in FX or futures\u003C/li\u003E\u003Cli\u003EExperience with alpha research, portfolio construction and optimization\u003C/li\u003E\u003Cli\u003EExperience building statistical/technical, fundamental, and data driven signals\u003C/li\u003E\u003Cli\u003EExperience synthesizing predictive signals for both cross-sectional and time-series models\u003C/li\u003E\u003Cli\u003EStrong experience with data exploration, dimension reduction, and feature engineering\u003C/li\u003E\u003Cli\u003EThorough understanding of and comfort using a variety of regression techniques—including OLS, MLS, Ridge, Lasso, and Bayesian inference—as well as techniques for dealing with errors that can occur, such as auto-correlation and heteroskedasticity\u003C/li\u003E\u003Cli\u003EExperience managing and running risk is a strong plus\u003C/li\u003E\u003Cli\u003EProficiency in Python using the machine learning stack—numpy, pandas, scikit-learn, etc.\u003C/li\u003E\u003Cli\u003ECreative mindset\u003C/li\u003E\u003Cli\u003EStrong time management ability—the ability to manage multiple tasks and deadlines in a fast-paced environment\u003C/li\u003E\u003Cli\u003EHigh degree of drive and energy—must be a self-starter\u003C/li\u003E\u003Cli\u003EAbility to work cooperatively with all levels of staff and to thrive in a team-oriented environment\u003C/li\u003E\u003Cli\u003ECommitment to the highest ethical standards and who act with professionalism and integrity at all times\u003C/li\u003E\u003C/ul\u003E\u003Cbr\u003E\u003Cp\u003EThe annual base salary range for this role is $150,000-$200,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.\u003C/p\u003E\u003Cbr\u003E\",\"Japanese_Job_Description_External__c\":\"\u003Cbr\u003E\u003Cbr\u003E\u003Cbr\u003E\",\"Transcript_Optional__c\":false,\"RecordTypeId\":\"012j0000000tcfpAAA\",\"Apply_Now_URL__c\":\"https://grnh.se/baadf90d2us\",\"Type__c\":\"Full Time\",\"LastModifiedDate\":\"2026-01-07T17:34:08.000+0000\",\"Location__c\":\"New York, New York\",\"Company__r\":{\"attributes\":{\"type\":\"Account\",\"url\":\"/services/data/v65.0/sobjects/Account/001j000000VbgA3AAJ\"},\"Business__c\":\"Cubist\",\"Name\":\"Cubist Systematic Strategies, LLC\",\"Id\":\"001j000000VbgA3AAJ\",\"RecordTypeId\":\"012j0000000tIlgAAE\"},\"RecordType\":{\"attributes\":{\"type\":\"RecordType\",\"url\":\"/services/data/v65.0/sobjects/RecordType/012j0000000tcfpAAA\"},\"DeveloperName\":\"Cubist\",\"Name\":\"Cubist\",\"Id\":\"012j0000000tcfpAAA\"}},\"friendlyJobName\":\"quantitative-researcher-macro\",\"formattedTeam\":\"Systematic Investing\",\"formattedLocation\":\"New York\",\"formattedArea\":\"Investing\"}');
Join Cubist to help build a new systematic macro business focused on mid-frequency alpha strategies across futures, FX, and volatility. You will develop and implement systematic trading models, perform alpha research and backtesting, evaluate new datasets, and contribute to production/trading infrastructure and execution monitoring. The role requires strong quantitative skills, experience with data exploration and feature engineering, and proficiency in Python and the ML stack. It is an early-career research role within a quantitative investing team.
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
Quantitative researcher to help build out a new systematic macro (futures, FX, and vol) business. Core focus will be working on mid-frequency alpha strategies.
Job Description
Develop systematic trading models across FX, commodities, fixed income, and equity markets
Alpha idea generation, backtesting, and implementation
Assist in building, maintenance, and continual improvement of production and trading environments
Evaluate new datasets for alpha potential
Improve existing strategies and portfolio optimization
Execution monitoring
Be a core contributor to growing the investment process and research infrastructure of the team
Desirable Candidates
Masters or PhD in mathematics, statistics, physics or other quantitative discipline. PhD in statistics or machine learning is a plus
Experience in quantitative trading, ideally in FX or futures
Experience with alpha research, portfolio construction and optimization
Experience building statistical/technical, fundamental, and data driven signals
Experience synthesizing predictive signals for both cross-sectional and time-series models
Strong experience with data exploration, dimension reduction, and feature engineering
Thorough understanding of and comfort using a variety of regression techniques—including OLS, MLS, Ridge, Lasso, and Bayesian inference—as well as techniques for dealing with errors that can occur, such as auto-correlation and heteroskedasticity
Experience managing and running risk is a strong plus
Proficiency in Python using the machine learning stack—numpy, pandas, scikit-learn, etc.
Creative mindset
Strong time management ability—the ability to manage multiple tasks and deadlines in a fast-paced environment
High degree of drive and energy—must be a self-starter
Ability to work cooperatively with all levels of staff and to thrive in a team-oriented environment
Commitment to the highest ethical standards and who act with professionalism and integrity at all times
The annual base salary range for this role is $150,000-$200,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
// Mixpanel ignore tracking for known IPs var excludeIPS = '[65.213.72.30, 185.142.16.9, 203.176.115.9, 208.68.197.6, 208.68.197.9, 208.68.199.6, 208.68.199.9, 208.85.160.9, 208.85.161.9]'; $.getJSON('//api.ipify.org?format=json', function(data) { try{ if(true && excludeIPS.indexOf(data.ip) > -1){ console.log('Mixpanel ignore events set: NO events tracked.'); mixpanel.register({"$ignore":true}); }else{ mixpanel.unregister("$ignore"); mixpanel.track("View Page", { "Page Name": document.querySelector('.dotted-underline') ? document.querySelector('.dotted-underline').innerText : location.href, "Careers Site": true }); mixpanel.people.set_once({ 'First Career Page Visit' : new Date().toISOString() }); mixpanel.people.set({ 'Last Career Page Visit' : new Date().toISOString() }); // Only for Careers Site mixpanel.people.increment("# of Career Page Visits"); (function(){ var links = document.querySelectorAll('a'); [].forEach.call(links, function(link) { link.addEventListener("click", function (e) { mixpanel.track("Click Link", { "Link Name": link.text, "Link Location": link.getAttribute('link-location') == null ? 'Body' : link.getAttribute('link-location'), "Link Type": link.getAttribute('link-type') == null ? '' : link.getAttribute('link-type'), "Link Destination URL" : link.href }); }); }); })(); } }catch(e){} }); jQuery(document).ready(function(){ var str = navigator.userAgent; if (str.toLowerCase().indexOf("firefox") >= 0) { jQuery('body').addClass("gecko"); } }); {"employmentType":"FULL_TIME","identifier":{"name":"Cubist Systematic Strategies, LLC","@type":"PropertyValue"},"jobLocation":[{"address":{"addressCountry":"US","addressRegion":"New York","addressLocality":"New York","@type":"PostalAddress"},"@type":"Place"}],"hiringOrganization":{"sameAs":"https://www.point72.com/","name":"Cubist","@type":"Organization"},"datePosted":"2026-01-19","description":"
About Cubist
\n
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
\n
Quantitative researcher to help build out a new systematic macro (futures, FX, and vol) business. Core focus will be working on mid-frequency alpha strategies.
Job Description
\n
Develop systematic trading models across FX, commodities, fixed income, and equity markets
Alpha idea generation, backtesting, and implementation
Assist in building, maintenance, and continual improvement of production and trading environments
Evaluate new datasets for alpha potential
Improve existing strategies and portfolio optimization
Execution monitoring
Be a core contributor to growing the investment process and research infrastructure of the team
Desirable Candidates
\n
Masters or PhD in mathematics, statistics, physics or other quantitative discipline. PhD in statistics or machine learning is a plus
Experience in quantitative trading, ideally in FX or futures
Experience with alpha research, portfolio construction and optimization
Experience building statistical/technical, fundamental, and data driven signals
Experience synthesizing predictive signals for both cross-sectional and time-series models
Strong experience with data exploration, dimension reduction, and feature engineering
Thorough understanding of and comfort using a variety of regression techniques—including OLS, MLS, Ridge, Lasso, and Bayesian inference—as well as techniques for dealing with errors that can occur, such as auto-correlation and heteroskedasticity
Experience managing and running risk is a strong plus
Proficiency in Python using the machine learning stack—numpy, pandas, scikit-learn, etc.
Creative mindset
Strong time management ability—the ability to manage multiple tasks and deadlines in a fast-paced environment
High degree of drive and energy—must be a self-starter
Ability to work cooperatively with all levels of staff and to thrive in a team-oriented environment
Commitment to the highest ethical standards and who act with professionalism and integrity at all times
The annual base salary range for this role is $150,000-$200,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
","title":"Quantitative Researcher - Macro","@type":"JobPosting","@context":"http://schema.org/"} CSJobDetailModule.init('{\"lastModifiedDateFormatted\":\"2026-01-07\",\"job\":{\"attributes\":{\"type\":\"Job__c\",\"url\":\"/services/data/v65.0/sobjects/Job__c/a035b00001HcPQaAAN\"},\"Id\":\"a035b00001HcPQaAAN\",\"Name\":\"Quantitative Researcher - Macro\",\"Assigned_Internal_Recruiter__c\":\"005j000000EWCJ4AAP\",\"Job_Code__c\":\"CSS-0008107\",\"Experience__c\":\"Early Career\",\"Company__c\":\"001j000000VbgA3AAJ\",\"Posted_Location__c\":\"New York\",\"Area__c\":\"Investing\",\"Team__c\":\"Systematic Investing\",\"Summary__c\":\"Senior quantitative researcher to help build out a new systematic macro (futures, FX, and vol) business. Core focus will be working on mid-frequency alpha strategies.\",\"Job_Description_External__c\":\"\u003Ch3\u003EAbout Cubist\u003C/h3\u003E\\n\u003Cp\u003ECubist 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.\u003C/p\u003E\u003Cbr\u003E\u003Ch3\u003ERole\u003C/h3\u003E\\n\u003Cp\u003EQuantitative researcher to help build out a new systematic macro (futures, FX, and vol) business. Core focus will be working on mid-frequency alpha strategies.\u003C/p\u003E\u003Cbr\u003E\u003Ch3\u003EJob Description\u003C/h3\u003E\\n\u003Cul\u003E\u003Cli\u003EDevelop systematic trading models across FX, commodities, fixed income, and equity markets\u003C/li\u003E\u003Cli\u003EAlpha idea generation, backtesting, and implementation\u003C/li\u003E\u003Cli\u003EAssist in building, maintenance, and continual improvement of production and trading environments\u003C/li\u003E\u003Cli\u003EEvaluate new datasets for alpha potential\u003C/li\u003E\u003Cli\u003EImprove existing strategies and portfolio optimization\u003C/li\u003E\u003Cli\u003EExecution monitoring\u003C/li\u003E\u003Cli\u003EBe a core contributor to growing the investment process and research infrastructure of the team\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003E\u003Cbr\u003E\u003C/p\u003E\u003Cbr\u003E\u003Ch3\u003EDesirable Candidates\u003C/h3\u003E\\n\u003Cul\u003E\u003Cli\u003EMasters or PhD in mathematics, statistics, physics or other quantitative discipline. PhD in statistics or machine learning is a plus\u003C/li\u003E\u003Cli\u003EExperience in quantitative trading, ideally in FX or futures\u003C/li\u003E\u003Cli\u003EExperience with alpha research, portfolio construction and optimization\u003C/li\u003E\u003Cli\u003EExperience building statistical/technical, fundamental, and data driven signals\u003C/li\u003E\u003Cli\u003EExperience synthesizing predictive signals for both cross-sectional and time-series models\u003C/li\u003E\u003Cli\u003EStrong experience with data exploration, dimension reduction, and feature engineering\u003C/li\u003E\u003Cli\u003EThorough understanding of and comfort using a variety of regression techniques—including OLS, MLS, Ridge, Lasso, and Bayesian inference—as well as techniques for dealing with errors that can occur, such as auto-correlation and heteroskedasticity\u003C/li\u003E\u003Cli\u003EExperience managing and running risk is a strong plus\u003C/li\u003E\u003Cli\u003EProficiency in Python using the machine learning stack—numpy, pandas, scikit-learn, etc.\u003C/li\u003E\u003Cli\u003ECreative mindset\u003C/li\u003E\u003Cli\u003EStrong time management ability—the ability to manage multiple tasks and deadlines in a fast-paced environment\u003C/li\u003E\u003Cli\u003EHigh degree of drive and energy—must be a self-starter\u003C/li\u003E\u003Cli\u003EAbility to work cooperatively with all levels of staff and to thrive in a team-oriented environment\u003C/li\u003E\u003Cli\u003ECommitment to the highest ethical standards and who act with professionalism and integrity at all times\u003C/li\u003E\u003C/ul\u003E\u003Cbr\u003E\u003Cp\u003EThe annual base salary range for this role is $150,000-$200,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.\u003C/p\u003E\u003Cbr\u003E\",\"Japanese_Job_Description_External__c\":\"\u003Cbr\u003E\u003Cbr\u003E\u003Cbr\u003E\",\"Transcript_Optional__c\":false,\"RecordTypeId\":\"012j0000000tcfpAAA\",\"Apply_Now_URL__c\":\"https://grnh.se/baadf90d2us\",\"Type__c\":\"Full Time\",\"LastModifiedDate\":\"2026-01-07T17:34:08.000+0000\",\"Location__c\":\"New York, New York\",\"Company__r\":{\"attributes\":{\"type\":\"Account\",\"url\":\"/services/data/v65.0/sobjects/Account/001j000000VbgA3AAJ\"},\"Business__c\":\"Cubist\",\"Name\":\"Cubist Systematic Strategies, LLC\",\"Id\":\"001j000000VbgA3AAJ\",\"RecordTypeId\":\"012j0000000tIlgAAE\"},\"RecordType\":{\"attributes\":{\"type\":\"RecordType\",\"url\":\"/services/data/v65.0/sobjects/RecordType/012j0000000tcfpAAA\"},\"DeveloperName\":\"Cubist\",\"Name\":\"Cubist\",\"Id\":\"012j0000000tcfpAAA\"}},\"friendlyJobName\":\"quantitative-researcher-macro\",\"formattedTeam\":\"Systematic Investing\",\"formattedLocation\":\"New York\",\"formattedArea\":\"Investing\"}');