Campus Recruiter for Machine Learning & Quantitative Research

About the Position Jane Street is seeking a talented Campus Recruiter who will play a pivotal role in shaping our recruitment strategy and expanding our efforts to attract, interview, and onboard exceptional interns and new graduates for our Research and Machine Learning Research teams. This position will involve planning, organizing, and executing our internship programs at our New York office. Finding the right talent is crucial to our success. Our innovative recruitment strategy and engaging interview process are designed to identify and hire the brightest minds from around the globe. As our campus recruitment initiatives grow rapidly, we are looking for strategic thinkers to help us remain competitive. You will excel in managing day-to-day candidate interactions while collaborating with the Recruiting team and other departments at Jane Street to enhance our recruiting approach and identify future directions. Our success in recruitment is driven by our attention to detail, responsiveness, and the connections we build with candidates. You should be highly organized, detail-oriented, and possess a proactive attitude, ready to tackle tasks of all sizes. Your ability to combine efficiency with genuine empathy for candidates will make you an invaluable member of our team. About You 8+ years of professional recruiting experience, with strong preference for those who have managed summer internship programs. Enthusiastic about learning new skills and making an immediate impact. Excellent communication skills to interact clearly with colleagues, candidates, and search firms. Approachable and humble, willing to seek assistance and acknowledge mistakes. Self-motivated with the ability to manage multiple projects and priorities simultaneously. Curious about the firm's operations and how recruiting fits into the larger context. No prior experience in the financial industry is required. If you are a recruiting agency interested in partnering with us, please contact agency-partnerships@janestreet.com.

About BasisBasis is a pioneering nonprofit organization dedicated to applied AI research. Our mission is twofold: to deepen our understanding of intelligence and to empower society in solving complex, challenging problems.Our first objective is to comprehend and construct intelligence. This involves establishing foundational mathematical principles for reasoning, learning, decision-making, comprehension, and explanation, while also developing software that embodies these principles.Secondly, we aim to enhance society’s capability to tackle intractable issues. This includes broadening the scope and complexity of problems we can address today and accelerating our future problem-solving abilities.To accomplish these objectives, we are creating a technological infrastructure inspired by human reasoning and fostering a collaborative organization that prioritizes human values.About the RoleAs a Research Scientist, you will spearhead Basis’ initiatives to unravel the conceptual, mathematical, and computational foundations of intelligence. We seek individuals with exceptional technical skills who are eager to explore foundational concepts. Our research scientists and engineers are committed to conducting rigorous, high-quality scientific investigations, while also embracing the opportunity to experiment, learn from errors, and explore innovative ideas.Collaboration is integral to our work, both internally and with external partners. We are searching for individuals who thrive in team settings, tackling challenges that transcend individual capabilities.Key Expectations:Proven ability to conduct high-quality scientific research, demonstrated through publications, technical reports, or software projects.Preferred Qualifications:PhD or equivalent experience in relevant fields such as statistics, programming languages, machine learning, computational neuroscience, cognitive science, physics, or mathematics.Enthusiasm for addressing real-world challenges and contributing to positive societal change.ResponsibilitiesDevelop and investigate computational theories of intelligence, encompassing reasoning, learning, and decision-making.Collaborate closely with domain experts, both within Basis and externally, to contribute to solving scientific problems.

About BasisBasis is a pioneering nonprofit organization dedicated to applied AI research, driven by dual objectives that enhance each other.Our primary focus is to understand and construct intelligence. This encompasses establishing the fundamental mathematical principles of reasoning, learning, decision-making, comprehension, and explanation, alongside developing software that embodies these principles.Secondly, we aim to empower society to tackle complex challenges. This involves broadening the scale, complexity, and scope of problems we can currently address and, crucially, accelerating our future problem-solving capabilities.To realize these ambitions, we are establishing an innovative technological framework inspired by human reasoning and fostering a collaborative environment that prioritizes human values.About the RoleAs a Research Engineer, you will play a vital role in advancing Basis’ mission by converting research concepts into accurate, robust, and scalable high-quality code.We are looking for technically proficient individuals who are enthusiastic about delving deep into foundational concepts. Our research engineers are committed to conducting rigorous, high-quality science, unafraid to experiment, learn from mistakes, and explore innovative ideas.Basis thrives on collaboration, both within our team and with external partners. We seek individuals who enjoy tackling challenges greater than they can manage alone.Machine Learning Research Engineer Focus AreasThis position is aimed at experts in machine learning engineering. Key areas of focus include:Probabilistic programming and statistical inferenceDeep learningCausal inferenceProgram synthesis and analysisML Ops and systems engineeringThese areas will be explored in the context of developing reasoning systems. Research engineers will also engage with topics including programming language design and implementation, automatic differentiation, and SAT/SMT solvers.

Join Virtu Financial, a leading quantitative trading firm, where innovation meets impact in the financial markets. As a Machine Learning Researcher, you will engage in groundbreaking research opportunities within a dynamic and agile organization. This role presents a unique blend of intellectual challenge and immediate business influence, as you navigate complex problems that lack straightforward solutions.Your responsibilities will encompass our entire modeling ecosystem, from feature engineering and deep learning architecture to training dynamics and execution strategies. Your pioneering contributions will shape our market operations globally, making a real difference in a fast-evolving industry that thrives on creative problem-solving and first-principles thinking.We seek individuals with a strong sense of curiosity, a robust technical skill set, and a collaborative spirit, regardless of their industry background.

About the Position Jane Street is seeking intelligent and inquisitive minds to join our expanding Machine Learning team and lead our innovative ML initiatives. As a Machine Learning Researcher, you will develop advanced deep learning models that enhance our trading strategies, supported by our powerful computing cluster featuring tens of thousands of high-performance GPUs. The trading landscape presents unique challenges—such as complex models and dynamic datasets within a competitive multi-agent setting—driving us to explore cutting-edge methodologies. At Jane Street, our researchers, engineers, and traders collaborate closely, often just a few feet apart, to train models, design systems, and execute trading strategies. Each day may involve analyzing market data, optimizing hyperparameters, troubleshooting distributed training performance, or assessing our models' real-world trading behavior. Your extensive knowledge of the machine learning ecosystem and familiarity with diverse methodologies—ranging from LLMs, image models, reinforcement learning agents, recommendation systems, to classical ML techniques—will be instrumental in shaping the future of ML at Jane Street. You will be responsible for training models that will inform the next generation of our deep learning-driven trading strategies, as well as building the foundational insights necessary for navigating new markets and situations. Additionally, you will engage in hiring new talent, attending conferences, and sharing knowledge with teammates—all considered vital aspects of your role.

About the PositionJane Street is seeking talented Quantitative Researchers to assist in developing models, strategies, and systems for pricing and trading financial instruments. You will collaborate closely with seasoned researchers dedicated to mentoring our newest team members, immersing yourself in experimental design, dataset generation, time series analysis, feature engineering, and model construction for financial datasets.At Jane Street, our researchers, engineers, and traders work in close proximity, fostering a collaborative environment to train models, design systems, and execute trading strategies. We leverage petabytes of data and operate on a computing cluster with hundreds of thousands of cores, alongside a rapidly expanding GPU cluster featuring tens of thousands of high-performance GPUs. Your daily tasks may include delving into market data, fine-tuning hyperparameters, debugging distributed training performance, or analyzing our model's trading behavior in production settings.We reject the notion of a “one-size-fits-all” modeling approach; instead, we embrace a wide array of statistical and machine learning techniques, from linear models to deep learning, adapting our methods to meet the specific needs of each problem. The most successful researchers thrive on their curiosity about how their contributions integrate into the broader framework of our trading operations, transforming their findings into actionable strategies.About YouIf you’ve never considered a career in finance, you’re not alone—many of our team members were in the same boat before joining us. If you possess a curious mind and a passion for tackling intriguing challenges, you will likely feel at home here. Ideal candidates will:Utilize logical and mathematical reasoning to approach diverse problemsExhibit intellectual curiosity; eager to ask questions, acknowledge mistakes, and pursue new knowledgeBe proficient in programming, particularly with PythonCommunicate precisely and think openly, enjoying collaboration with colleagues across various fields and expertiseWhile most candidates have a background in data science or machine learning, we prioritize your thought process and learning capability over specific knowledge. A PhD or relevant research experience is advantageous.For more insights, feel free to explore our interview process and meet some of the team.

Position OverviewJane Street is seeking talented Quantitative Researchers to join our innovative team. In this role, you will develop advanced models, strategies, and systems for pricing and trading financial instruments. Your expertise in experimental design, dataset generation, time series analysis, feature engineering, and model building will be applied to financial datasets, all while collaborating with a team of experts who will challenge and enhance your methodologies.At Jane Street, our researchers, engineers, and traders work closely together in a dynamic environment, leveraging petabytes of data and a vast GPU cluster equipped with tens of thousands of high-performance GPUs. Your day might involve deep analysis of market data, tuning hyperparameters, debugging distributed training issues, or evaluating how our models perform in real-world trading scenarios.We value diversity in our modeling solutions, embracing a broad range of statistical and machine learning techniques, from linear models to deep learning, tailored to meet the unique challenges we face. The most successful researchers will possess a strong curiosity about how their work contributes to our trading operations and how to translate their insights into actionable strategies.

At Trexquant, a cutting-edge systematic hedge fund, we leverage a multitude of statistical algorithms to navigate global equity, futures, and other markets. Our approach harnesses extensive datasets to create comprehensive feature sets, utilizing advanced machine learning techniques to unearth trading signals and integrate them into market-neutral portfolios. We are actively seeking talented individuals from diverse backgrounds, including data science, physics, engineering, economics, and programming, to pioneer the next wave of machine learning strategies that adeptly forecast the movements of liquid financial assets.As a Quantitative Researcher, you will engage in the development of market-neutral signals, meticulously parse and analyze large datasets, and collaborate with the Data and Strategy Research team to construct a variety of predictive models. While we welcome expertise across all asset classes, our immediate focus lies on equities, futures, commodities, and event-driven research.

About BasisBasis is a pioneering nonprofit organization dedicated to applied AI research, driven by two key objectives.The first objective is to comprehend and develop intelligence. This encompasses establishing the mathematical foundations of reasoning, learning, decision-making, understanding, and explanation, while also creating software that embodies these principles.The second objective is to enhance society’s capacity to tackle complex challenges. This involves broadening the scope, scale, and complexity of problems we can address today, and crucially, accelerating our capacity to solve future problems.To fulfill these missions, we are constructing an innovative technological infrastructure inspired by human reasoning, along with a collaborative organization that prioritizes human values.About the RoleAs an ML Systems Engineer at Basis, you will ensure that our training and evaluation infrastructure is fast, reliable, and scalable. You will manage the entire stack, from distributed training frameworks to cloud administration, enabling researchers to rapidly iterate on complex models while efficiently managing computational resources.We are seeking engineers who possess a profound understanding of ML systems paired with operational excellence. The ideal candidate will have experience in distributed training at scale, expertise in debugging numerical instabilities, and the ability to manage cloud infrastructure that seamlessly transitions from experimentation to production. You will be the steward of training stability, an optimizer of computational costs, and a facilitator of reproducible research.This position encompasses both traditional ML engineering and cloud/DevOps responsibilities. You will oversee GPU clusters, optimize cloud expenditures, ensure security and compliance, and build the infrastructure that allows researchers to focus on algorithms rather than operations.We are looking for individuals who are committed to developing robust ML infrastructure, maintaining a culture of documentation for issues and solutions, and prioritizing operational excellence as a core value.

OverviewAt OXMAN, a pioneering nature-focused research and design firm based in Manhattan, we leverage nature-centric solutions to tackle pressing challenges at the intersection of Nature and Humanity. Our interdisciplinary approach spans various sectors, fostering innovative concepts that redefine the interaction between the built environment and the natural ecosystem.Our initiative, EDEN, is dedicated to applied ecological systems operating on architectural, urban, and landscape scales. We are at the forefront of creating a digital design framework that engineers biodiverse and resilient ecosystems by developing software that empowers designers and engineers to incorporate essential ecosystem services—such as carbon sequestration, thermal buffering, and biodiversity—into their master planning and design processes. Our mission is to elevate ecosystem services from undervalued byproducts to vital infrastructure elements. We invite talented professionals to join us in revolutionizing the future of nature-centric design.We are currently seeking a Machine Learning Research Engineer to integrate into our dynamic design team in New York City. This role will be pivotal in supporting our efforts to deliver exceptional, data-driven design proposals utilizing EDEN’s innovative suite of ecosystem engineering tools.

Hello! I’m Aidan, the founder of Maple.At Maple, we are on a mission to revolutionize local businesses by creating intelligent AI agents. Our agents efficiently manage tasks such as answering calls, taking orders, and booking appointments, providing seamless interactions through natural voice communication.Our deeper mission: We are developing automated ontologies that accurately reflect the operational realities of businesses — capturing their services, workflows, constraints, and unique language — allowing our agents to adapt instantly to diverse business needs. We strive to meet businesses where they are, rather than imposing a rigid software framework.With a growing customer base, strong revenue growth, a secure financial runway, and support from esteemed investors, we are positioned for exciting advancements. More details will be shared during our conversation.Role OverviewAs a Machine Learning Research Engineer at Maple, you will be integral to our core product team, transforming innovative research into production-ready voice agents that facilitate millions of customer interactions for small businesses. You will collaborate with experts from prestigious institutions such as Google Brain, Two Sigma, Stanford, MIT, Columbia, and IBM to swiftly deploy sophisticated models and systems that have a direct impact on small enterprises.Our team operates in person, five days a week in our vibrant NYC office. Our collaborative environment is characterized by rapid-paced, dynamic interactions filled with trust and transparency. We embrace a culture of speed and adaptability, tackling challenges head-on.Key ResponsibilitiesEnhance automatic speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS) systems for practical applications, ensuring high accuracy in diverse and noisy environments.Fine-tune LLMs utilizing retrieval-augmented generation (RAG), reinforcement learning (RL), and prompt engineering for engaging, contextually aware dialogues.Integrate AI components into autonomous agents capable of executing complex tasks such as scheduling, order management, and problem resolution.Design human-in-the-loop and automated systems for monitoring performance, identifying anomalies, and iteratively improving models based on real-world feedback.Develop pipelines for building knowledge graphs from business data, enhancing dynamic AI interactions.Collaborate with infrastructure teams to scale models effectively, ensuring robust performance and reliability.

Become part of an innovative team that is pioneering the development of the world's first biological reasoning model. Collaborate with us to create generative foundational models that unravel biological systems on various scales—from molecules to entire organisms—empowering us to predict, comprehend, and manipulate living systems in unprecedented ways.Output Biosciences is currently operating in stealth mode, led by a team of experienced founders and biotech experts with a proven track record in AI and biology, supported by top-tier venture capital firms including Y Combinator.As a Machine Learning Research Scientist specializing in Interpretability, you will collaborate closely with our founders and dedicated team to create AI systems that can process and reason across diverse biological data modalities simultaneously.Develop specialized techniques to enhance the understanding of our biological reasoning models, reverse-engineering their representation and processing of complex biological information.Design and perform experiments aimed at discovering interpretable features across various biological scales.Tackle unique challenges in biological model interpretation, including disentangling multi-scale, hierarchical representations of biological systems.Create biology-specific visualization tools that effectively map model activations to meaningful biological concepts.

Quick FactsRole: Quantitative ResearcherLocation: 5 days/week @ NYC HQBase Salary: $200K to $325KEquity: Competitive initial grant plus annual performance-based bonusesAbout MomentMoment is at the forefront of developing innovative trading and portfolio management technology. Our flagship product suite encompasses high-throughput market data pipelines, automated smart order routing algorithms, real-time portfolio ledgering and position tracking, as well as advanced portfolio optimization techniques.Founded in 2023 by a dynamic team of quantitative traders and researchers from Citadel and Jane Street, we have successfully secured over $100 million in funding from notable investors including Andreessen Horowitz and Index Ventures. Our technology supports critical operations for financial institutions managing assets exceeding $8 trillion.The Research Team at Moment tackles complex challenges such as:Executing over 100K variable portfolio optimizations within seconds.Creating machine learning models to predict relative value and future performance of fixed income securities.Formulating risk models to assess the tracking error across portfolios.Developing AI agents to conduct credit research, construct custom portfolios, and automate essential portfolio management functions.You're a Great Fit If...You are a quantitative researcher or trader, with a willingness to consider strong candidates from other fields.You possess a bachelor's degree or PhD in Mathematics, Physics, Statistics, Economics, or Computer Science.You have experience writing production-ready code in Python.You are resourceful and eager to tackle challenges.You enjoy collaborating closely with clients; at Moment, researchers also act as product managers.Preferred QualificationsExperience in fixed income quantitative research.Familiarity with numerical optimization techniques.Knowledge of factor and risk models.Experience with Polars.Proficiency in machine learning pipelines.Experience with multi-modal LLMs.BenefitsHealth Insurance401k

Become a pivotal part of our innovative team at Output Biosciences, where you will contribute to the groundbreaking development of the world's inaugural biological reasoning model. Collaborate with us in creating generative foundational models that decode biological systems at all scales—from molecules to entire organisms—allowing us to predict, understand, and manipulate living systems in unprecedented ways.Currently operating under stealth mode, Output Biosciences is spearheaded by a team of accomplished founders and seasoned biotech veterans with a proven track record of successful exits in both AI and biology. We are proudly backed by leading venture capital firms including Y Combinator.As a Machine Learning Research Scientist specializing in Generative AI, you will work closely with our founders and collaborative team members to forge advanced generative models capable of synthesizing and designing biological systems across various scales. Building on our biological reasoning model, your focus will be on creating generative capabilities that translate insights into innovative applications.Design and develop state-of-the-art generative models tailored for biological data, enabling AI systems to generate novel molecular structures, protein designs, cellular components, and biological pathways.Implement cutting-edge generative algorithms including diffusion models, language diffusion models, diffusion transformers, and flow matching approaches, customized for biological constraints and properties.Train and scale pioneering AI models utilizing large, diverse multimodal biological datasets across distributed GPU clusters.Solve complex AI research challenges at scale, addressing crucial aspects like model architecture, scaling, pre-training and fine-tuning strategies, and rigorous evaluation methods.Explore and implement various advanced generative AI architectures, such as transformers, diffusion models, and autoencoders, to push the boundaries of biological AI innovation.

Join Virtu’s Research Technology team as an accomplished Machine Learning Engineer. You'll be part of a dynamic group of specialists focused on creating the robust infrastructure that fuels our quantitative researchers. This is a rare chance to engage at the crossroads of machine learning and systematic trading, developing tools that directly influence the efficiency and speed of our research capabilities. In this pivotal role, you will spearhead the development of our machine learning research platform. Your responsibilities will include managing data and computational resources, tracking experiments, and empowering researchers to transition from concept to result with maximum efficiency. Collaborating closely with quantitative analysts and engineers, you will significantly impact the implementation of machine learning practices as we enhance our operational capabilities. Our tech stack primarily includes Python, C++, and Java, complemented by various open-source tools and proprietary technologies.

About BasisBasis is a nonprofit organization dedicated to applied AI research, striving to achieve two interconnected objectives.Firstly, we aim to understand and develop intelligence. This involves establishing the mathematical foundations of reasoning, learning, decision-making, comprehension, and explanation; along with creating software that embodies these principles.Secondly, we seek to enhance society's capacity to address complex challenges. This means broadening the range, scale, and intricacies of the problems we can tackle today, while also accelerating our ability to solve future challenges.To fulfill these missions, we are constructing a novel technological framework inspired by human reasoning, and fostering a collaborative organization that prioritizes human values.About the RoleAs a Research Scientist, you will spearhead Basis's initiatives to deepen our comprehension of the theoretical, mathematical, and computational aspects of intelligence.We seek individuals with exceptional technical skills who are passionate about exploring concepts at their core. Our research scientists and engineers are committed to conducting rigorous, high-quality, and robust scientific inquiry, yet they embrace experimentation, learning from mistakes, and exploring innovative ideas to achieve their goals.Basis thrives on collaboration, both internally and with external partners; thus, we value team players who relish tackling challenges that surpass individual capabilities.Research FocusOur research under the MARA project is dedicated to forging new principles and technologies for modeling, abstraction, and reasoning in AI systems. The primary aim of MARA is to reveal principled methodologies for how intelligence constructs, refines, and applies world models through interactive experimentation.For this position, we specifically seek experts in Reinforcement Learning & Planning who can push the boundaries of model-based RL, exploration strategies, optimal control, and Bayesian optimization. You will develop agents capable of learning efficient policies in intricate, partially observable environments by utilizing structured world models.

About the PositionJoin our dynamic team at Jane Street as a Machine Learning Researcher, where your intelligence and curiosity will be pivotal in advancing our machine learning initiatives.On the Machine Learning team, you will develop sophisticated deep learning models that underpin our trading strategies. You will have access to an expansive computing cluster, equipped with tens of thousands of powerful GPUs. The trading domain presents unique challenges, including large-scale models and nonstationary datasets within a competitive multi-agent environment, compelling us to explore innovative techniques.At Jane Street, our researchers, engineers, and traders collaborate closely, sitting just a few feet apart to refine models, construct systems, and execute trading strategies. Your day could involve delving into market data, fine-tuning hyperparameters, troubleshooting distributed training performance, or analyzing trading behaviors of our models in real time.We will depend on your extensive knowledge of the machine learning landscape and your familiarity with diverse methodologies, whether they stem from LLMs, image models, reinforcement learning agents, recommendation systems, or traditional ML approaches. You will contribute to training models for our next-generation deep learning-based trading strategies and help cultivate the foundational insights necessary for navigating new markets and scenarios. Additionally, you will be involved in recruiting new team members, participating in conferences, and imparting your expertise to colleagues—all considered vital and impactful aspects of this role.About YouIf a career in finance is new to you, you’re not alone; many of our team members started from similar backgrounds. If you possess a curious mindset and a passion for tackling intriguing problems, we believe you will thrive here. While there isn’t a fixed skill set we are seeking, ideal candidates should possess:Hands-on experience with empirical machine learning challengesThe capacity to apply logical and mathematical reasoning to various problemsA strong intellectual curiosity and enthusiasm for cutting-edge research across multiple ML domainsProficiency in utilizing a diverse range of models and techniquesPractical coding skills to quickly implement and iterate on your concepts using Python and your preferred ML frameworkA willingness to ask questions, acknowledge mistakes, and embrace new learning experiencesFor further insights, take a look at our interview process and meet our team.

Join Jane Street as a Senior Weather Analyst and Machine Learning Researcher, where your expertise will contribute to advanced weather prediction models and data analysis. You will collaborate with a talented team to leverage machine learning techniques in analyzing complex meteorological datasets, improving our forecasting capabilities.

Join our dynamic Volatility team at Trexquant as a highly skilled Quantitative Researcher. This crucial role involves collaborating closely with the Head of Volatility to expand our research group's capabilities, focusing on the development of specialized volatility tools and the exploration of trading signals and strategies within the volatility markets. The ideal candidate will possess a strong background in volatility modeling, statistical analysis, and a comprehensive understanding of market dynamics.Key Responsibilities:Develop and maintain proprietary pricing and analytics tools tailored for volatility research.Calibrate implied volatility surfaces across a range of options including single stock, index, and ETF options, while working with developers to implement models into backtesting and live trading systems.Design, implement, and enhance trading strategies aimed at predicting volatility market trends using extensive financial datasets and diverse trading signals.Analyze large datasets to uncover actionable alpha signals and devise effective volatility trading strategies.Investigate and leverage innovative academic research in quantitative finance to evaluate, enhance, and maximize the profitability of trading strategies.Continuously refine existing models by incorporating new data sources and advanced methodologies to improve performance and scalability.Collaborate with a team of seasoned quantitative researchers to conduct experiments, backtest hypotheses, and fine-tune strategies through comprehensive simulations and data analysis.

Our MissionAt comity, we are dedicated to revolutionizing energy systems by enhancing their reliability, transparency, and efficiency. Our goal is to pave the way for a future characterized by sustainable and abundant energy. To achieve this, we employ cutting-edge statistical learning and convex optimization techniques (AI) to construct the financial frameworks essential for tomorrow's energy systems.We envision a world where energy systems are efficient, autonomous, resilient, and powered entirely by renewable energy sources. About UsFounded by industry veterans from Apple, Bluevine, Affirm, Square, and Google, our leadership team comprises Stanford alumni who possess deep expertise in complex systems, machine learning, and structured finance. Supported by esteemed investors like Maverick Ventures and Caffeinated Capital, we are aligned with our strategic objectives and vision for the platform.Your RoleWe are seeking a Quantitative Researcher for Monetization to assist in designing, deploying, and operating autonomous, systematic strategies that extract economic value from our future information (forecasts) and energy systems models, while navigating real-world market constraints. In this role, you will:Identify and prioritize market opportunities based on potential value and complexity.Architect end-to-end autonomous strategies encompassing models, forecasts, and market actions.Enhance strategy performance by utilizing metadata to inform bidding decisions.Manage the full process from information development to production-ready code, stepping in when additional resources are needed.What We Value in YouYou have applied stochastic optimization to challenges within financial or electrical engineering, operations research, or economics.You possess a strong conceptual understanding of probability theory and an aptitude for shaping and managing distributions.You bring over 5 years of design, research, and development experience within the industry.You have a profound understanding of the dynamics and structure of energy, commodity, or financial markets.You are proficient in developing and monitoring machine learning models.You are a skilled programmer, particularly in Python.Most importantly, you are driven and passionate about making a significant impact in the energy sector.