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Applied ML for Trading Signals","count":1}],"locations":[{"location":"new york, ny","count":4},{"location":"new york, new york, united states of america","count":4},{"location":"hong kong","count":1}],"skills":[{"skill":"Python","required":9,"preferred":0,"total":9},{"skill":"NumPy","required":5,"preferred":0,"total":5},{"skill":"Pandas","required":4,"preferred":0,"total":4},{"skill":"Polars","required":4,"preferred":0,"total":4},{"skill":"Market Microstructure","required":1,"preferred":2,"total":3},{"skill":"Intraday Trading Dynamics","required":1,"preferred":2,"total":3},{"skill":"SciPy","required":2,"preferred":0,"total":2},{"skill":"Intellectual Curiosity","required":1,"preferred":1,"total":2},{"skill":"Agentic AI Workflows","required":0,"preferred":2,"total":2},{"skill":"Data Analysis","required":1,"preferred":1,"total":2},{"skill":"Linux","required":2,"preferred":0,"total":2},{"skill":"Machine Learning","required":2,"preferred":0,"total":2},{"skill":"scikit-learn","required":2,"preferred":0,"total":2},{"skill":"Probability Theory","required":1,"preferred":1,"total":2},{"skill":"C++","required":2,"preferred":0,"total":2},{"skill":"Apache Arrow","required":1,"preferred":1,"total":2},{"skill":"Columnar Data Formats","required":1,"preferred":1,"total":2},{"skill":"Kdb+/q","required":0,"preferred":2,"total":2},{"skill":"Regression","required":1,"preferred":1,"total":2},{"skill":"Masters Or PhD Degree In A Quantitative Subject","required":1,"preferred":0,"total":1}],"salary_stats":{"count":8,"min":175000,"q1":175000,"median":175000,"q3":175000,"max":175000},"job_types":[{"type":"full-time","count":9}],"latest_jobs":[{"id":"job_cd6a4f33-c1c","title":"Quantitative Researcher, Quantitative Strategies","source_url":"https://mlp.eightfold.ai/careers/job/755957362218","location":"Hong Kong","job_type":"full-time","experience_level":"mid","work_arrangement":null,"category":"Finance","description":"<p>We are seeking a Quantitative Researcher to join our team in a collaborative environment, focusing on Asian equity markets.</p>\n<p>The ideal candidate combines quantitative skills with hands-on trading or desk quant experience in Asian equities.</p>\n<p><strong>Principal Responsibilities</strong></p>\n<ul>\n<li>Develop and implement systematic equity trading strategies in Asian markets, including China Mainland, Japan, Hong Kong, and Taiwan</li>\n<li>Apply knowledge of Asian equity market rules, trading conventions, and venue characteristics to optimize strategy deployment</li>\n<li>Conduct quantitative research in alpha signals: idea generation, data processing, and statistical analysis</li>\n<li>Implement trading strategies across multiple venues</li>\n<li>Carry out market-specific analysis</li>\n</ul>\n<p><strong>Preferred Technical Skills</strong></p>\n<ul>\n<li>Expertise in Python</li>\n<li>Proficiency in modern data science tools (Jupyter, pandas, NumPy, scikit-learn)</li>\n<li>Degree in Mathematics, Computer Science, Statistics, or related STEM field from a top-ranked university</li>\n<li>Demonstrated knowledge of quantitative finance, mathematical modeling, statistical analysis, regression, and probability theory</li>\n<li>Excellent communication, problem-solving, and analytical skills</li>\n</ul>\n<p><strong>Preferred Experience</strong></p>\n<ul>\n<li>Direct trading or desk quant experience in Asian equity markets, with practical knowledge of market rules and trading conventions</li>\n<li>Minimum 2 years of experience in a quantitative trading, desk quant, or quantitative research role with exposure to live equity trading</li>\n<li>Experience working with multiple datasets and manipulating data (assessing quality, cleaning, creating features, etc.)</li>\n</ul>\n<p><strong>Highly Valued Relevant Experience</strong></p>\n<ul>\n<li>Hands-on experience with live systematic equity trading strategies in Asian markets</li>\n<li>Entrepreneurial mindset, self-motivated, detail-oriented, and able to work independently in a fast-paced environment</li>\n<li>Curiosity and critical thinking</li>\n<li>Eagerness to learn and grow professionally</li>\n</ul>\n<p><strong>Target Start Date</strong>: ASAP</p>","enriched_at":1784024211928},{"id":"job_f5c18273-a46","title":"Quantitative Researcher -Data Infrastructure & Signal Development","source_url":"https://mlp.eightfold.ai/careers/job/755956531966","location":"New York, NY","job_type":"full-time","experience_level":"mid","work_arrangement":"onsite","category":"Finance","description":"<p>We are seeking a versatile quantitative researcher with strong data engineering skills to join a newly formed systematic equities pod focused on intraday mean reversion and market microstructure strategies.</p>\n<p>You will be responsible for building and maintaining the research data infrastructure, and for developing and testing trading signals using statistical and machine learning methods. This role combines data engineering rigor with quantitative research creativity.</p>\n<p>Principal Responsibilities:</p>\n<ul>\n<li>Build and maintain the research data pipeline: ingestion, cleaning, normalization, and storage of tick-level and minute-bar equity data</li>\n<li>Design and Implement a high-performance research environment using Python, Polars for interactive analysis of large datasets</li>\n<li>Develop, backtest, and validate intraday alpha signals using statistical methods and classical machine learning (Lasso, Ridge, tree-based models)</li>\n<li>Perform feature engineering on market microstructure data: order flow, spread dynamics, volume profiles, and cross-sectional patterns</li>\n<li>Build automated backtesting frameworks with realistic transaction cost modeling and slippage estimation</li>\n<li>Collaborate with the C++ developer to publish validated signals into the production trading engine</li>\n<li>Monitor live signal performance, detect regime changes, and maintain signal quality over time</li>\n<li>Document research findings, maintain reproducible research notebooks, and contribute to the team knowledge base</li>\n</ul>\n<p>Required Skills/ Qualifications:</p>\n<ul>\n<li>Bachelor&#39;s or Master&#39;s degree in Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or a related quantitative field</li>\n<li>3+ years of experience in a quantitative research or data-intensive role in a buy-side or sell-side financial firm</li>\n<li>Strong programming skills in Python with deep proficiency in Polars, Pandas, NumPy, and SciPy</li>\n<li>Solid understanding of statistical methods: regression, time-series analysis, hypothesis testing, cross-validation</li>\n<li>Familiarity with equity markets, market microstructure, and intraday trading dynamics</li>\n<li>Strong data engineering instincts: schema design, data quality, pipeline reliability</li>\n<li>Detail-oriented with strong problem-solving skills and intellectual curiosity</li>\n<li>Excellent communication skills and ability to work In a small, fast-paced team</li>\n</ul>\n<p>Preferred Skills / Experience:</p>\n<ul>\n<li>Experience with tick-level or order-book data analysis</li>\n<li>Familiarity with Apache Arrow, Parquet, and columnar data formats</li>\n<li>Experience with kdb+/q for time-series data</li>\n<li>Familiarity with Al-assisted development tools (Cursor, Claude Code)</li>\n</ul>\n<p>Millennium offers a total compensation package which includes a base salary, discretionary performance bonus, and comprehensive benefits. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future. When finalizing an offer, we take into consideration an individual’s experience level and the qualifications they bring to the role to formulate a competitive total compensation package.</p>","enriched_at":1781017808446},{"id":"job_b78a0e85-9f1","title":"Quantitative Developer, C++ I Low-Latency Systems","source_url":"https://mlp.eightfold.ai/careers/job/755956532578","location":"New York, NY","job_type":"full-time","experience_level":"mid","work_arrangement":"onsite","category":"Engineering","description":"<p>We are seeking a highly skilled C++ developer to architect, build, and maintain the core signal computation and alpha Infrastructure for a newly formed systematic equities pod.</p>\n<p>You will own the hot path - real-time feature computation, signal generation, and Integration with the firm&#39;s shared execution and market data platforms - and play a foundational role in shaping the technology stack from day one.</p>\n<p>This is a hands-on role at the intersection of systems engineering and quantitative research. You will work directly with the Portfolio Manager and quantitative researchers to translate alpha signals into production-ready, high-performance trading systems.</p>\n<p>Principal Responsibilities:</p>\n<ul>\n<li>Design and build the core C++ signal engine: real-time feature computation, alpha signal generation, position tracking, and risk monitoring</li>\n</ul>\n<ul>\n<li>Architect the data bridge between the C++ hot path and the Python/Polars research layer</li>\n</ul>\n<ul>\n<li>Implement and optimize real-time alpha signal publication from the research pipeline into the firm&#39;s shared execution infrastructure</li>\n</ul>\n<ul>\n<li>Integrate with the firm&#39;s central market data feeds and execution platforms</li>\n</ul>\n<ul>\n<li>Develop real-time risk checks, position monitoring, logging, and alerting infrastructure</li>\n</ul>\n<ul>\n<li>Optimize system performance: latency profiling, lock-free data structures, memory management, and network tuning</li>\n</ul>\n<ul>\n<li>Collaborate closely with quantitative researchers to understand strategy requirements and translate prototypes (Python) into production-grade C++ code</li>\n</ul>\n<ul>\n<li>Leverage Al-assisted development tools (Cursor, Claude Code) to accelerate development velocity while maintaining code quality</li>\n</ul>\n<ul>\n<li>Build and maintain backtesting and exchange simulation infrastructure for strategy validation</li>\n</ul>\n<p>Required Skills / Qualifications:</p>\n<ul>\n<li>Bachelor&#39;s or Master&#39;s degree in Computer Science, Mathematics, Physics, Engineering, or a related quantitative field</li>\n</ul>\n<ul>\n<li>3+ years of hands-on experience developing high-performance C++ server-side applications in Linux</li>\n</ul>\n<ul>\n<li>Strong understanding of real-time and event-driven architectures with tight latency requirements</li>\n</ul>\n<ul>\n<li>Proficiency in Python with working knowledge of Polars, Pandas, NumPy, and the PyData ecosystem</li>\n</ul>\n<ul>\n<li>Deep familiarity with Apache Arrow and columnar data formats for cross-language interoperability</li>\n</ul>\n<ul>\n<li>Strong understanding of network programming, Linux OS internals, and systems optimization</li>\n</ul>\n<ul>\n<li>Experience consuming real-time market data feeds and integrating with shared execution platforms</li>\n</ul>\n<ul>\n<li>Solid understanding of data structures, algorithms, and concurrent/multithreaded programming</li>\n</ul>\n<ul>\n<li>Proficiency with Git, CI/CD, unit testing, and software engineering best practices</li>\n</ul>\n<p>Preferred Skills / Experience:</p>\n<ul>\n<li>Experience building trading systems in a systematic equities or quant trading environment</li>\n</ul>\n<ul>\n<li>Familiarity with low-latency optimization techniques: cache-friendly data structures, SIMD, memory-mapped I/O</li>\n</ul>\n<ul>\n<li>Experience with Rust for performance-critical systems development</li>\n</ul>\n<ul>\n<li>Experience with kdb+/q for time-series data</li>\n</ul>\n<ul>\n<li>Knowledge of equity market microstructure, order types, and execution algorithms</li>\n</ul>\n<ul>\n<li>Experience with DuckDB, Arrow Flight, or similar analytical database technologies</li>\n</ul>\n<ul>\n<li>Familiarity with cloud infrastructure (AWS) and containerized deployments</li>\n</ul>\n<p>Millennium offers a total compensation package which includes a base salary, discretionary performance bonus, and comprehensive benefits. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future.</p>","enriched_at":1781017797953},{"id":"job_37ed18a5-bb9","title":"ML Research Scientist -Deep Learning & Transformer Architectures","source_url":"https://mlp.eightfold.ai/careers/job/755956532395","location":"New York, NY","job_type":"full-time","experience_level":"senior","work_arrangement":"onsite","category":"Engineering","description":"<p>As part of a long-term research agenda within a newly formed systematic equities pod, we are building a proprietary Transformer-based model trained on tokenized intraday market data for next-token prediction of price movements. We are seeking an exceptional ML research scientist with deep expertise in Transformer architectures and large-scale model training.</p>\n<p>You will design, implement, and train a custom decoder-only Transformer from scratch -not fine-tune an existing LLM, but build a purpose-built architecture for financial time-series. This is a long-term research project with significant computational resources.</p>\n<p>Principal Responsibilities:</p>\n<ul>\n<li>Design and implement a custom decoder-only Transformer architecture optimized for tokenized financial time-series data</li>\n<li>Develop a novel tokenization scheme for intraday market data: price movements, volume, order flow, and cross-sectional features</li>\n<li>Implement efficient training pipelines using PyTorch with mixed-precision training, gradient checkpointing, and multi-GPU parallelism</li>\n<li>Design attention mechanisms adapted to financial data: temporal attention patterns, cross-asset attention, and multi-scale representations</li>\n<li>Build evaluation frameworks for next-token prediction accuracy, signal quality, and trading performance</li>\n<li>Implement inference optimization for low-latency production deployment: model quantization, KV-cache, speculative decoding</li>\n<li>Conduct rigorous ablation studies to validate architecture choices and training methodology</li>\n<li>Collaborate with the team to integrate model predictions into the live trading pipeline</li>\n<li>Document research methodology, experimental results, and architectural decisions</li>\n</ul>\n<p>Required Skills / Qualifications:</p>\n<ul>\n<li>PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field with a focus on deep learning</li>\n<li>Demonstrated ability to implement Transformer architectures from scratch (not just fine-tuning pre-trained models)</li>\n<li>Deep understanding of attention mechanisms, positional encodings, tokenization strategies, and training dynamics</li>\n<li>Expert-level PyTorch skills including custom modules, training loops, mixed-precision, and multi-GPU training</li>\n<li>Strong mathematical foundations: linear algebra, probability theory, optimization, information theory</li>\n<li>Experience training models at scale (100M+ parameters)</li>\n<li>Strong programming skills in Python and C++ for performance-critical components</li>\n<li>Self-directed researcher capable of defining and executing a multi-month research agenda</li>\n<li>Familiarity with Al-assisted development tools (Cursor, Claude Code)</li>\n</ul>\n<p>Preferred Skills / Experience:</p>\n<ul>\n<li>Experience applying deep learning to financial data or time-series forecasting</li>\n<li>Familiarity with tokenization approaches for continuous or non-text data</li>\n<li>Published research in top ML venues (NeurlPS, ICML, ICLR) or equivalent industry experience</li>\n<li>Knowledge of market microstructure and intraday trading dynamics</li>\n<li>Experience with model compression, quantization, and inference optimization</li>\n</ul>\n<p>Millennium offers a total compensation package which includes a base salary, discretionary performance bonus, and comprehensive benefits. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future.</p>","enriched_at":1781017786195},{"id":"job_43dff6d1-bde","title":"Machine Leaming Engineer - Applied ML for Trading Signals","source_url":"https://mlp.eightfold.ai/careers/job/755956532278","location":"New York, NY","job_type":"full-time","experience_level":"mid","work_arrangement":"onsite","category":"Engineering","description":"<p>We are seeking an applied ML engineer to develop, optimize, and deploy machine learning models for alpha generation within a newly formed systematic equities pod deploying intraday mean reversion and microstructure strategies.</p>\n<p>The focus is on tree-based ensemble methods (LightGBM, XGBoost, CatBoost) and classical ML pipelines applied to high-frequency financial data. You will work closely with the Portfolio Manager and quantitative researchers to turn ML models into live trading signals.</p>\n<p>Principal Responsibilities:</p>\n<ul>\n<li>Develop and optimize tree-based ensemble models (LightGBM, XGBoost, CatBoost) for intraday alpha prediction</li>\n<li>Design and implement end-to-end ML pipelines: feature engineering, training, validation, deployment, and monitoring</li>\n<li>Build robust cross-validation frameworks adapted to financial time-series (purged k-fold, walk-forward)</li>\n<li>Engineer features from market microstructure data: order flow imbalance, spread dynamics, volume patterns, cross-asset signals</li>\n<li>Implement model explainability tools (SHAP, feature importance) to understand and validate signal sources</li>\n<li>Optimize model inference for low-latency production deployment</li>\n<li>Monitor model performance in production: detect drift, staleness, and regime changes</li>\n<li>Collaborate with the C++ developer to integrate ML predictions into the real-time trading engine</li>\n<li>Experiment with TabPFN and other rapid-prototyping tools for fast signal discovery</li>\n</ul>\n<p>Required Skills / Qualifications:</p>\n<ul>\n<li>Master&#39;s degree in Computer Science, Statistics, Mathematics, Machine Learning, or a related quantitative field</li>\n<li>3+ years of experience building and deploying ML models in a production environment, preferably in finance</li>\n<li>Deep expertise in tree-based ensemble methods: LightGBM, XGBoost, CatBoost, including hyperparameter tuning, regularization, and feature selection</li>\n<li>Strong programming skills in Python with proficiency in scikit-learn, Polars/Pandas, NumPy</li>\n<li>Strong understanding of overfitting, data leakage, and proper evaluation methodology for financial time-series</li>\n<li>Strong analytical thinking, attention to detail, and intellectual curiosity</li>\n<li>Excellent communication skills and ability to explain model behavior to non-ML stakeholders</li>\n<li>Familiarity with Al-assisted development tools (Cursor, Claude Code)</li>\n</ul>\n<p>Preferred Skills / Experience:</p>\n<ul>\n<li>Experience with financial market data (tick data, order book, corporate actions)</li>\n<li>Knowledge of market microstructure and intraday trading dynamics</li>\n</ul>\n<p>Millennium offers a total compensation package which includes a base salary, discretionary performance bonus, and comprehensive benefits. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future. When finalizing an offer, we take into consideration an individual’s experience level and the qualifications they bring to the role to formulate a competitive total compensation package.</p>","enriched_at":1781017774932},{"id":"job_5170abf3-3ce","title":"Quantitative Analyst, Quantitative Strategies","source_url":"https://mlp.eightfold.ai/careers/job/755956329760","location":"New York, New York, United States of America","job_type":"full-time","experience_level":"mid","work_arrangement":"onsite","category":"Finance","description":"<p>We are seeking a Quantitative Analyst to join a small, collaborative team focused on systematic equity strategies. This role is well suited for someone who enjoys working at the intersection of equity research, alternative data, and quantitative modeling, with a particular focus on sector-specific signals, company-level KPIs, and custom dataset development.</p>\n<p>The role offers direct exposure to the SPM and broader investment process, with the opportunity to contribute across idea generation, data work, signal research, model implementation, and backtesting.</p>\n<p>Responsibilities: Partner closely with the SPM and team on alpha research for systematic equity strategies, with a focus on sector-specific signals and company-level forecasting Identify, collect, organize, and analyze structured and unstructured datasets relevant to company, product, and sector behavior Integrate Agentic AI workflows where they can improve productivity, model development, or operational robustness Develop creative, data-driven ways to track companies, industries, and products beyond traditional financial statement analysis Contribute to the research, implementation, and backtesting of signals using alternative, fundamental, and positioning datasets Apply sound financial intuition and statistical techniques to turn raw data into investable insights Work in a transparent, collaborative environment with visibility into the broader investment process</p>\n<p>Preferred Technical Skills: Bachelor’s, Master’s, or PhD in a quantitative field such as Mathematics, Statistics, Computer Science, Engineering, Economics, Physics, or a related discipline Strong Python skills Experience working with company- and sector-specific KPIs Experience using alternative, fundamental, and positioning datasets Practical experience using LLMs or modern ML tools in analytical, research, or dataset-building workflows is highly desirable Strong analytical, problem-solving, and communication skills</p>\n<p>Preferred Experience: 1–3 years of experience in data science, data analytics, quantitative research, or a related role Experience at an investment firm, data analytics firm, research provider, or software company is preferred Demonstrated ability to take analytical projects from conception through implementation</p>\n<p>Target Start Date: ASAP, though we will wait 6–9 months for an exceptional candidate</p>","enriched_at":1779955777267},{"id":"job_93ed5f5a-e8a","title":"Quantitative Developer, Quantitative Strategies","source_url":"https://mlp.eightfold.ai/careers/job/755956329525","location":"New York, New York, United States of America","job_type":"full-time","experience_level":"senior","work_arrangement":"onsite","category":"Engineering","description":"<p>We are seeking a highly skilled, entrepreneurial Quantitative Developer to join an existing collaborative quantitative trading pod. This is a hands-on role at the intersection of technology, data, research, and trading, with direct exposure to the Senior Portfolio Manager and quantitative researchers.</p>\n<p>The role spans the full systematic trading stack, with a particular focus on research infrastructure, data systems, signal deployment, and production monitoring.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Owning and continuously improving the team&#39;s research platform, including the backtesting framework, simulation environments, and caching/compute layers</li>\n<li>Building and maintaining tooling that enables researchers to develop, test, and deploy signals efficiently</li>\n<li>Integrating Agentic AI workflows where they can improve productivity, model development, or operational robustness</li>\n<li>Designing, organizing, and maintaining large-scale datasets and data pipelines used across research and production</li>\n<li>Optimizing and supporting the team&#39;s interfaces with central and external systems, including execution, risk monitoring, and compute/resource management</li>\n<li>Helping productionize and monitor trading signals, ensuring robustness, observability, and operational reliability</li>\n</ul>\n<p>Preferred technical skills include:</p>\n<ul>\n<li>Strong Python engineering skills, with the ability to write clean, scalable, production-quality code</li>\n<li>Experience with performance optimization in Python and with parallel/distributed workloads</li>\n<li>Familiarity with tools such as Kubernetes, Ray, Dask, Polars, Slurm, or similar distributed compute/orchestration frameworks</li>\n<li>Experience with SQL; familiarity with modern data warehouses such as Snowflake is a plus</li>\n<li>Strong Linux experience</li>\n<li>Solid understanding of system design, design patterns, and data architecture</li>\n</ul>\n<p>Preferred experience includes:</p>\n<ul>\n<li>3+ years of experience as a quantitative developer, research engineer, or software/data engineer, ideally in a systematic trading or financial context</li>\n<li>Experience building or supporting research platforms, simulation frameworks, or quantitative data infrastructure</li>\n<li>Experience creating, organizing, and maintaining custom datasets and production-grade data pipelines</li>\n<li>Experience supporting the deployment, monitoring, and maintenance of live research outputs or trading models</li>\n<li>Experience working closely with researchers in a fast-paced, iterative environment</li>\n</ul>\n<p>Millennium offers a total compensation package which includes a base salary, discretionary performance bonus, and comprehensive benefits. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future.</p>","enriched_at":1779955762072},{"id":"job_0b324db1-063","title":"Quantitative Researcher, Quantitative Strategies","source_url":"https://mlp.eightfold.ai/careers/job/755956328628","location":"New York, New York, United States of America","job_type":"full-time","experience_level":"mid","work_arrangement":"onsite","category":"Finance","description":"<p>We are seeking a Quantitative Researcher to join a small, collaborative team focused on systematic equity strategies. This role offers the opportunity to contribute across the full research lifecycle, including idea generation, data sourcing, signal development, model implementation, backtesting, and live strategy refinement, with forecasting horizons spanning intraday to several weeks.</p>\n<p>Principal Responsibilities: Partner closely with the SPM and team on alpha research for systematic equity strategies Generate and test new ideas using financial intuition, statistical learning, and large, diverse datasets Integrate Agentic AI workflows where they can improve productivity, model development, or operational robustness Source, clean, and analyze alternative, fundamental, and market microstructure data Build predictive models and contribute to signal combination, portfolio implementation, and ongoing model refinement Work in a transparent, collaborative environment with exposure to the broader investment process</p>\n<p>Preferred Technical Skills: Bachelor’s, Master’s, or PhD in a quantitative field such as Mathematics, Statistics, Computer Science, Physics, or a related STEM discipline Strong Python skills; experience building research tools or production-quality research infrastructure is highly desirable Experience developing systematic equity or statistical arbitrage alphas, including intraday rebalancing of multi-day horizon signals Experience working with alternative, fundamental, and exchange / market microstructure data Practical experience applying LLMs or modern ML techniques to research workflows, signal generation, or dataset creation is highly desirable</p>\n<p>Preferred Experience: Minimum 3 years of experience in quantitative research focused on systematic equities Strong preference for candidates from quantitative trading teams, though we are open to strong quantitative candidates from discretionary environments</p>\n<p>Target Start Date: ASAP, though we will wait up to 12 months for an exceptional candidate</p>","enriched_at":1779955745459},{"id":"job_55cce8b6-8ff","title":"Quantitative Researcher, Systematic Macro","source_url":"https://mlp.eightfold.ai/careers/job/755943671775","location":"New York, New York, United States of America","job_type":"full-time","experience_level":"mid","work_arrangement":"onsite","category":"Finance","description":"<p>A fast-growing, collaborative, and entrepreneurial systematic investment team is seeking a highly skilled Quantitative Researcher with expertise in systematic macro strategies.</p>\n<p>The ideal candidate will contribute to alpha research, signal development, and strategy implementation in a dynamic and fast-paced environment. This role offers significant career growth.</p>\n<p>Principal Responsibilities:</p>\n<p>Work closely with the Senior Portfolio Manager to develop systematic macro strategies, focusing on alpha research, including idea generation, data preprocessing, statistical analysis, backtesting, and implementation.</p>\n<p>Contribute to and enhance the internal research platform, including data pipelines, statistical learning tools, alpha analytics, and backtesting frameworks.</p>\n<p>Independently explore and develop new alpha ideas while collaborating in a transparent and team-oriented environment.</p>\n<p>Preferred Technical Skillset:</p>\n<p>Strong research and programming skills, with proficiency in Python.</p>\n<p>Solid experience with data analytics libraries (e.g., Pandas, SciPy, NumPy, Polars); extensive library-building experience is a plus.</p>\n<p>Masters or PhD degree in a quantitative subject such as Applied Mathematics, Statistics, Physics, Engineering, Financial Engineering, Computer Science, or related field from a top-ranked university. Strong candidates with Bachelor&#39;s degree will also be considered.</p>\n<p>Exceptional problem-solving abilities, intellectual curiosity (especially in alpha research), and a proactive research mindset.</p>\n<p>Creativity and out-of-the-box thinking, combined with rigorous quantitative analysis.</p>\n<p>Preferred Experience:</p>\n<p>2+ years of experience in quantitative research with a focus on systematic macro strategies.</p>\n<p>Preferred experience in hedge fund alpha research in commodities, FX, equity, and bond futures.</p>\n<p>Experience in macro intraday strategies is a strong plus.</p>\n<p>Experience in trading cost analysis is a plus.</p>\n<p>Experience in machine learning is a plus.</p>\n<p>Target Start Date:</p>\n<p>Up to 12 months (strong preference for candidates who can start sooner)</p>\n<p>Millennium pays a total compensation package which includes a base salary, discretionary performance bonus, and a comprehensive benefits package. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future.</p>","enriched_at":1776550454142}],"category_normalised":[{"category":"finance","count":5},{"category":"engineering","count":4}],"velocity":{"weeks":[{"week_start":"2026-04-13","count":1},{"week_start":"2026-05-25","count":3},{"week_start":"2026-06-08","count":4},{"week_start":"2026-07-13","count":1}],"trend":"stable","wow_pct":-75},"momentum":{"recent_14d":1,"prior_14d":0,"growth_pct":0,"classification":"stable"},"salary_vs_industry":{"company_median":175000,"industry_median":null,"percentile":null,"sample_size":7,"by_region":[{"region":"United States","company_median":175000,"industry_median":null,"sample":7}],"transparency_pct":78,"industry_transparency_pct":0,"transparency_warning":false},"market_share":{"company_jobs":9,"industry_total":9,"share_pct":100,"rank":1,"peer_count":1},"ai_exposure":{"occupation_weighted_score":0.22,"skill_weighted_score":0.22,"top_exposed_titles":[{"title":"Quantitative Researcher, Quantitative Strategies","count":2,"score":0.22}],"top_exposed_skills":[{"skill":"Python","count":5,"score":0.22},{"skill":"Pandas","count":3,"score":0.22},{"skill":"NumPy","count":3,"score":0.22}]},"peer_set":[],"skills_lq":[],"geographic_shift":{"current":[{"region":"United States","count":8,"share_pct":88.9},{"region":"APAC","count":1,"share_pct":11.1}],"emerging":[],"shrinking":[{"region":"United States","recent_30d":0,"prior_30d":7,"growth_pct":-100}]},"seniority_anomalies":{"exec_recent_30d":0,"exec_prior_90d_avg":0,"exec_growth_pct":0,"notable_exec_hires":[]},"posting_dynamics":{"median_days_open":null,"industry_median_days_open":null,"long_open_count":1,"closure_rate_pct":20}}}}