{"absolute_url":"https://www.janestreet.com/join-jane-street/apply/8617344002?gh_jid=8617344002","data_compliance":[{"type":"gdpr","requires_consent":false,"requires_processing_consent":false,"requires_retention_consent":false,"retention_period":null,"demographic_data_consent_applies":false}],"education":"education_optional","internal_job_id":6441601002,"location":{"name":"New York, New York, United States"},"metadata":[{"id":4246316002,"name":"Employment Type","value":"Summer Internship","value_type":"single_select"},{"id":4453104002,"name":"Event Date","value":null,"value_type":"date"},{"id":4453107002,"name":"Event Location","value":null,"value_type":"short_text"},{"id":4453108002,"name":"Event Time","value":null,"value_type":"short_text"},{"id":4453114002,"name":"Event Type","value":null,"value_type":"single_select"},{"id":4453115002,"name":"Event Continent","value":null,"value_type":"single_select"},{"id":4453117002,"name":"Event URL","value":null,"value_type":"short_text"},{"id":19843245002,"name":"Duration","value":"May-August","value_type":"single_select"},{"id":20495683002,"name":"Event Registration Deadline","value":null,"value_type":"date"},{"id":24844645002,"name":"Min salary","value":"300,000","value_type":"short_text"},{"id":24844702002,"name":"Max salary","value":"300,000","value_type":"short_text"},{"id":31769182002,"name":"Workday Job Profile","value":"Trading Intern","value_type":"single_select"}],"id":8617344002,"updated_at":"2026-11-03T10:18:06-05:00","requisition_id":"R-1371","title":"Quantitative Trader","company_name":"Jane Street","first_published":"2026-07-06T15:03:49-04:00","language":"en","application_deadline":null,"content":"\u0026lt;h3\u0026gt;About the Position\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;Our goals are to give you a real sense of what it\u0026#39;s like to work as a Quantitative Trader at Jane Street while also providing a truly unparalleled educational experience. You\u0026#39;ll be paired with experienced quantitative traders who will teach you how to identify market signals, analyze and execute strategies, construct quantitative models, conduct statistical analysis, and build trading intuition.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;At Jane Street, the lines between research, technology, and trading are intentionally blurry, and you’ll have access to petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing tens of thousands of high-end GPUs. We don\u0026#39;t believe in \u0026quot;one-size-fits-all\u0026quot; modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. You\u0026#39;ll work closely with two different mentors on projects relating to their day-to-day work, giving you a sense of the variety of problems we solve every day. Past projects have included analyzing new or existing datasets, training predictive models, simulating potential new trading strategies, writing tools that we use in production, and even working to answer big-picture questions we haven\u0026#39;t yet figured out.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;During the internship, your work is reinforced with intensive classes, workshops, and team-based mock trading sessions. These will expose you to many of the dynamics we observe in real markets, illustrate the role that we play in making markets more efficient, and help build intuition for how we think about both trading and collaborating.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;As a quantitative trading intern, you\u0026#39;ll also have the opportunity to participate in one \u0026quot;elective\u0026quot; based on your interests. Electives consist of targeted classes and immersive activities, and are designed to give you a deeper and more nuanced look into one of the many aspects of what quantitative trading can look like at Jane Street:\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Machine Learning, Modeling, and Data Science\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;You\u0026#39;ll learn how Jane Street applies advanced machine learning and statistical techniques to\u0026amp;nbsp;make models and predictions using large datasets of both real and simulated market data. You\u0026#39;ll learn how to train and use a variety of ML models, and gain an understanding of the differences between textbook machine learning and its application to noisy and complex financial data.\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Algorithmic Trading and Market Microstructure\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;You\u0026#39;ll learn the end-to-end process of developing an algorithmic trading strategy. You\u0026#39;ll analyze market data to develop a tradable fair value and implement a trading strategy in Python. Your algorithmic strategy will connect directly to simulated markets with different market structures, and you will learn how to optimize your strategy given the unique attributes of each market. You will discover how various market dynamics affect strategy behavior and learn how real-world trading differs from simulation.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Trading Strategy and Scenarios\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;You’ll be introduced to a rotating set of new trading scenarios inspired by real events on a particular trading desk. You’ll work in teams on multiple mock trading sessions related to each scenario and use the time between sessions to refine your strategies, write recaps, and hear how the story played out in real life from our seasoned full-time traders who lived through it.\u0026lt;/p\u0026gt;\n\u0026lt;h3\u0026gt;About You\u0026lt;/h3\u0026gt;\n\u0026lt;p\u0026gt;If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. If you have a curious mind, a collaborative spirit, and a passion for solving interesting problems, we have a feeling you’ll fit right in. We’re more interested in how you think and learn than what you currently know. You should be:\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;A strong quantitative thinker (no specific degree or major is required)\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;A clear and effective verbal and written communicator\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Someone who enjoys working collaboratively on a team\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Eager to ask questions, admit mistakes, and learn new things\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;A profitable trading strategy is only as strong as the technology it runs on, and we consider ourselves as much a technology company as a trading firm. General programming experience is a plus, but knowing a particular programming language is not required.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;If you’d like to learn more, you can read about our \u0026lt;a href=\u0026quot;https://www.janestreet.com/join-jane-street/interviewing/#quantitative-trading\u0026quot;\u0026gt;interview process\u0026lt;/a\u0026gt; and \u0026lt;a href=\u0026quot;https://www.janestreet.com/join-jane-street/get-to-know-us/\u0026quot;\u0026gt;meet some of the team\u0026lt;/a\u0026gt;. Learn more about Jane Street’s \u0026lt;a href=\u0026quot;https://www.janestreet.com/join-jane-street/internships/\u0026quot;\u0026gt;internship program \u0026lt;/a\u0026gt;here.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;em\u0026gt;If you\u0026#39;re a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com.\u0026lt;/em\u0026gt;\u0026lt;/p\u0026gt;","departments":[{"id":4073091002,"name":"Quantitative Trading","child_ids":[],"parent_id":4026211002}],"offices":[{"id":4013330002,"name":"New York","location":"New York, New York, United States","child_ids":[],"parent_id":null}],"ai_disclaimer":null,"include_ai_disclaimer":null,"ai_opt_out_request_url":null,"compliance":null,"demographic_questions":null,"questions":[{"description":null,"label":"First Name","required":true,"fields":[{"name":"first_name","type":"input_text","values":[]}]},{"description":null,"label":"Last Name","required":true,"fields":[{"name":"last_name","type":"input_text","values":[]}]},{"description":null,"label":"Preferred First Name","required":false,"fields":[{"name":"preferred_name","type":"input_text","values":[]}]},{"description":null,"label":"Email","required":true,"fields":[{"name":"email","type":"input_text","values":[]}]},{"description":null,"label":"Phone","required":true,"fields":[{"name":"phone","type":"input_text","values":[]}]},{"description":null,"label":"Resume/CV","required":true,"fields":[{"name":"resume","type":"input_file","values":[]},{"name":"resume_text","type":"textarea","values":[]}]},{"description":null,"label":"Cover Letter","required":false,"fields":[{"name":"cover_letter","type":"input_file","values":[]},{"name":"cover_letter_text","type":"textarea","values":[]}]},{"description":null,"label":"How did you hear about us?","required":true,"fields":[{"name":"question_37169537002","type":"multi_value_single_select","values":[{"label":"3Blue1Brown","value":246997693002},{"label":"Academic advisor","value":246997694002},{"label":"Academic camp...","value":246997695002},{"label":"Academic department","value":246997696002},{"label":"Advent of Code","value":246997697002},{"label":"AoPS - Art of Problem Solving","value":246997698002},{"label":"BEAM - Bridge to Enter Advanced Mathematics","value":246997699002},{"label":"Bright Network","value":246997700002},{"label":"CMD-IT; Tapia Conference","value":246997701002},{"label":"ColorStack","value":246997702002},{"label":"Computerphile","value":246997703002},{"label":"Cracking the Cryptic","value":246997704002},{"label":"Current employee...","value":246997705002},{"label":"Current/former intern","value":246997706002},{"label":"DS4A","value":246997707002},{"label":"ETC","value":246997708002},{"label":"Estimathon","value":246997709002},{"label":"Facebook","value":246997710002},{"label":"Faculty referral...","value":246997711002},{"label":"Figgie event","value":246997712002},{"label":"Figgie online","value":246997713002},{"label":"Game night","value":246997714002},{"label":"Glassdoor","value":246997715002},{"label":"Grace Hopper Celebration","value":246997716002},{"label":"GradCracker","value":246997717002},{"label":"HireWomenStem","value":246997718002},{"label":"ICFP","value":246997719002},{"label":"ICML","value":246997720002},{"label":"Info session","value":246997721002},{"label":"Jane Street Get to Know Us","value":246997722002},{"label":"Jane Street T-shirt or item","value":246997723002},{"label":"Jane Street Blog","value":246997724002},{"label":"Jane Street Tech Talk on YouTube","value":246997725002},{"label":"Jane Street games","value":246997726002},{"label":"Jane Street puzzles","value":246997727002},{"label":"Job Openings for Economists (JOE)","value":246997728002},{"label":"Kaggle","value":246997729002},{"label":"Lesbians Who Tech Summit","value":246997730002},{"label":"LinkedIn","value":246997731002},{"label":"MLT","value":246997732002},{"label":"Math Association of America","value":246997733002},{"label":"Mathfest","value":246997734002},{"label":"Mathjobs.org","value":246997735002},{"label":"Mystery Planet","value":246997736002},{"label":"NCWIT","value":246997737002},{"label":"NSBE","value":246997738002},{"label":"Networking dinner","value":246997739002},{"label":"NeurIPS","value":246997740002},{"label":"Numberphile","value":246997741002},{"label":"OSDI","value":246997742002},{"label":"Online forum...","value":246997743002},{"label":"Other Conference...","value":246997744002},{"label":"Other...","value":246997745002},{"label":"PLDI","value":246997746002},{"label":"POCIT","value":246997747002},{"label":"POPL","value":246997748002},{"label":"Per Scholas","value":246997749002},{"label":"Perspektywy Women in Tech","value":246997750002},{"label":"Piazza","value":246997751002},{"label":"Pre-university academic competition (IOI, IMO, HMMT, AIME, etc.)...","value":246997752002},{"label":"PuzzleCity","value":246997753002},{"label":"PyCon","value":246997754002},{"label":"REU program","value":246997755002},{"label":"Rare Recruitment (UK)","value":246997756002},{"label":"Real Numbers","value":246997757002},{"label":"Ron Brown Scholar Program","value":246997758002},{"label":"SREcon","value":246997759002},{"label":"STEM Women (UK organization)","value":246997760002},{"label":"Scientific Association of Mathematical Finance","value":246997761002},{"label":"StandUp Maths","value":246997762002},{"label":"Steve Mould","value":246997763002},{"label":"StrangeLoop","value":246997764002},{"label":"Student Freedom Initiative","value":246997765002},{"label":"Tech Talks","value":246997766002},{"label":"Twitter/X","value":246997767002},{"label":"University club...","value":246997768002},{"label":"University job board","value":246997769002},{"label":"University-hosted event (e.g., career fair, career services event, etc.)...","value":246997770002},{"label":"University-level competition (ICPC, Putnam, IMC, etc.)...","value":246997771002},{"label":"WeChat","value":246997772002},{"label":"Word of mouth","value":246997773002},{"label":"Working Not Working","value":246997774002},{"label":"Oxford Online Maths Club","value":246997775002},{"label":"CVPR","value":246997776002},{"label":"EMNLP","value":246997777002},{"label":"NANOG","value":246997778002},{"label":"PyTorch","value":246997779002},{"label":"SIGCOMM","value":246997780002},{"label":"Women in Logic","value":246997781002},{"label":"Jane Street OCaml Unboxed","value":246997782002},{"label":"Jane Street's Signals \u0026 Threads podcast","value":246997783002},{"label":"Periodic Videos","value":246997784002},{"label":"Reconcilable Differences","value":246997785002},{"label":"The Art of the Problem","value":246997786002},{"label":"HackerNews","value":246997787002},{"label":"Sixty Symbols","value":246997788002},{"label":"Dwarkesh Podcast","value":246997789002},{"label":"Oxford Mathematics Plus","value":246997790002},{"label":"Bsides","value":246997791002},{"label":"Progress Conference","value":246997792002},{"label":"TLDR AI","value":246997793002},{"label":"APS","value":246997794002},{"label":"CU*IP","value":246997795002},{"label":"FCCM","value":246997796002},{"label":"FP Matsuri","value":246997797002},{"label":"FPL","value":246997798002},{"label":"ICLR","value":246997799002},{"label":"ISFPGA","value":246997800002},{"label":"Latch-up Fossi","value":246997801002},{"label":"NDC London","value":246997802002},{"label":"OOPSLA/Splash","value":246997803002},{"label":"Out for Undergrad","value":246997804002},{"label":"Powershell Conf EU","value":246997805002},{"label":"Powershell Summit","value":246997806002},{"label":"PPL","value":246997807002},{"label":"Rust Asia","value":246997808002},{"label":"STOC","value":246997809002},{"label":"ALT","value":246997810002},{"label":"COLM","value":246997811002},{"label":"COLT","value":246997812002},{"label":"FAST","value":246997813002},{"label":"MLSys","value":246997814002},{"label":"NSDI","value":246997815002},{"label":"OOPSLA","value":246997816002},{"label":"Powershell Conference EU","value":246997817002},{"label":"SOSP","value":246997818002},{"label":"SPLASH","value":246997819002},{"label":"VLDB","value":246997820002},{"label":"Afrotech","value":254272426002},{"label":"ASAP","value":254272432002},{"label":"DBT Summit","value":254272433002},{"label":"LPAR","value":254272434002},{"label":"NixCon","value":254272435002},{"label":"Welch Labs","value":256471449002}]}]},{"description":null,"label":"Additional information (for source)","required":false,"fields":[{"name":"question_37169538002","type":"input_text","values":[]}]},{"description":null,"label":"Have you interviewed with Jane Street before?","required":true,"fields":[{"name":"question_37169539002","type":"multi_value_single_select","values":[{"label":"Yes","value":1},{"label":"No","value":0}]}]},{"description":null,"label":"Pronouns","required":false,"fields":[{"name":"question_37169540002","type":"input_text","values":[]}]},{"description":null,"label":"Are you currently a student?","required":true,"fields":[{"name":"question_37169541002","type":"multi_value_single_select","values":[{"label":"Yes","value":1},{"label":"No","value":0}]}]},{"description":null,"label":"Enter your college/university","required":false,"fields":[{"name":"question_37169542002","type":"input_text","values":[]}]},{"description":null,"label":"Major/Field of study","required":false,"fields":[{"name":"question_37169543002","type":"input_text","values":[]}]},{"description":"\u003cp\u003eWhy you\u0026rsquo;re interested in Jane Street\u003cbr /\u003eHow you heard about us\u003cbr /\u003eIf you have a job now, why you\u0026rsquo;re looking for a new one\u003c/p\u003e","label":"Extra","required":false,"fields":[{"name":"question_37169544002","type":"textarea","values":[]}]},{"description":null,"label":"Year you expect to begin full time employment","required":false,"fields":[{"name":"question_37169545002","type":"input_text","values":[]}]},{"description":null,"label":"What is your current education level?","required":false,"fields":[{"name":"question_37169546002","type":"multi_value_single_select","values":[{"label":"High School","value":246997821002},{"label":"Undergraduate","value":246997822002},{"label":"Master's","value":246997823002},{"label":"PhD","value":246997824002},{"label":"JD","value":246997825002},{"label":"MD","value":246997826002},{"label":"Other","value":246997827002}]}]},{"description":null,"label":"What year did you graduate high school?","required":false,"fields":[{"name":"question_37169547002","type":"input_text","values":[]}]},{"description":null,"label":"LinkedIn profile","required":false,"fields":[{"name":"question_37169548002","type":"input_text","values":[]}]},{"description":null,"label":"Website UID","required":false,"fields":[{"name":"question_37169549002","type":"input_text","values":[]}]},{"description":null,"label":"Who is your current employer/position?","required":false,"fields":[{"name":"question_37169550002","type":"input_text","values":[]}]},{"description":null,"label":"Languages Spoken","required":false,"fields":[{"name":"question_37169551002[]","type":"multi_value_multi_select","values":[{"label":"Spanish","value":246997828002},{"label":"Portuguese","value":246997829002},{"label":"French","value":246997830002},{"label":"Italian","value":246997831002},{"label":"German","value":246997832002},{"label":"Japanese","value":246997833002},{"label":"Korean","value":246997834002},{"label":"Bahasa Indonesian","value":246997835002},{"label":"Mandarin","value":246997836002},{"label":"Hindi","value":246997837002},{"label":"Other","value":246997838002}]}]},{"description":null,"label":"Additional Information For Languages","required":false,"fields":[{"name":"question_37169552002","type":"input_text","values":[]}]},{"description":null,"label":"University Email Address","required":false,"fields":[{"name":"question_37169553002","type":"input_text","values":[]}]},{"description":"\u003cp\u003e\u003c!--StartFragment--\u003eDo you have any recruiting timelines we should be aware of?\u003c!--EndFragment--\u003e\u003c/p\u003e","label":"Recruiting Timelines","required":false,"fields":[{"name":"question_38034279002","type":"multi_value_single_select","values":[{"label":"Yes","value":1},{"label":"No","value":0}]}]},{"description":"\u003cp\u003ePlease share more details about the relevant timeline\u003c/p\u003e","label":"Recruiting Timelines Details","required":false,"fields":[{"name":"question_38034284002","type":"input_text","values":[]}]},{"description":"\u003cp\u003eDo you require a visa or entry permit for your interviews?\u003c/p\u003e","label":"Visitor Visa","required":false,"fields":[{"name":"question_38034293002","type":"multi_value_single_select","values":[{"label":"Yes","value":1},{"label":"No","value":0}]}]},{"description":null,"label":"Secondary Major","required":false,"fields":[{"name":"question_38385379002","type":"multi_value_single_select","values":[{"label":"Aerospace Engineering","value":254333634002},{"label":"Actuarial Science","value":254333641002},{"label":"Anthropology","value":254333651002},{"label":"Applied Mathematics","value":254333663002},{"label":"Artificial Intelligence / Machine Learning","value":254333681002},{"label":"Biology","value":254333709002},{"label":"Business","value":254333735002},{"label":"Business Analytics","value":254333770002},{"label":"Chemistry","value":254333787002},{"label":"Computer Architecture","value":254333800002},{"label":"Computer Engineering","value":254333813002},{"label":"Computer Science","value":254333829002},{"label":"Computer Science and Artificial Intelligence","value":254333861002},{"label":"Computer Science and Electrical Engineering","value":254333891002},{"label":"Computer Science and Mathematics","value":254333917002},{"label":"Computer Science and Physics","value":254333926002},{"label":"Computer Science and Statistics","value":254333942002},{"label":"Cyber Security","value":254333956002},{"label":"Data Engineering","value":254333978002},{"label":"Data Science","value":254334009002},{"label":"Earth Sciences","value":254334028002},{"label":"Economics","value":254334039002},{"label":"Electrical Engineering","value":254334048002},{"label":"Engineering","value":254334056002},{"label":"Engineering Mathematics","value":254334070002},{"label":"Engineering Physics","value":254334078002},{"label":"Environmental Studies","value":254334086002},{"label":"Finance","value":254334098002},{"label":"Information Systems","value":254334107002},{"label":"Languages","value":254334123002},{"label":"Law","value":256513245002},{"label":"Linguistics","value":254334131002},{"label":"Manufacturing and Mechanics","value":254334138002},{"label":"Mathematics","value":254334145002},{"label":"Mathematics and Data Science","value":254334151002},{"label":"Mathematics and Physics","value":254334158002},{"label":"Mathematics and Statistics","value":254334162002},{"label":"Medicine","value":254334167002},{"label":"Natural Sciences","value":254334184002},{"label":"Naval Science","value":254334191002},{"label":"Operations Research / Financial Engineering","value":254334197002},{"label":"Organizational Theory","value":254334204002},{"label":"Philosophy","value":254334209002},{"label":"Physics","value":254334213002},{"label":"Political Science","value":254334223002},{"label":"Psychology","value":254334226002},{"label":"Quantitative Finance","value":254334230002},{"label":"Robotics","value":254334235002},{"label":"Software Engineering","value":254334238002},{"label":"Statistics","value":254334243002},{"label":"Statistics and Decision Theory","value":254334247002},{"label":"Veterinary Medicine","value":254334251002},{"label":"Other","value":254334255002}]}]},{"description":null,"label":"High School","required":false,"fields":[{"name":"question_38912798002","type":"input_text","values":[]}]}],"location_questions":[]}