{"id":3261930,"url":"https://alion.io/job/millennium-management-quantitative-equity-risk-analyst","title":"Quantitative Equity Risk Analyst","company":{"id":704283,"name":"Millennium Management","domain":"mlp.com","url":"https://alion.io/company/millennium-management","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"A","score":85,"open_postings":63,"ghost_share":0,"stale_share":0.413,"repost_share":0,"time_to_fill_p50_days":88,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Finance","role_family":"Finance","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tokyo, Japan"],"countries":["JP"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":62000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":3830},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-21T00:00:00Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-28T05:10:31Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"Quantitative Equity Risk AnalystAbout Millennium\nMillennium is a global, diversified alternative investment firm, founded in 1989. Defined by evolution, innovation and focus, Millennium’s mission is to deliver results for our investors.\nOur people are empowered with both independence and support: the autonomy to pursue ideas with conviction and the backing of a global network committed to collaboration, disciplined risk management and continuous learning. With opportunities to deepen expertise and accelerate development, talent at Millennium is equipped to adapt, evolve and build lasting impact over time. Discover how transformative growth accelerates impact.\nMeet the Team\nMillennium’s Global Risk Management Department identifies, measures, monitors, manages and reports risk across the firm’s portfolios. The Tokyo-based team is part of the broader Asia and global risk organization, partnering with colleagues in Hong Kong and across the firm to support a rigorous, collaborative approach to market, credit and liquidity risk management.\nWhat You'll Do\nConduct quantitative assessments of equity portfolio management activities, with a focus on performance metrics and risk factors.\nDevelop and enhance risk-analysis approaches that deepen insight and identify potential blind spots.\nEvaluate portfolio manager strategies and processes using quantitative and AI-driven methods.\nDesign, build and implement visualization tools and AI and machine learning models to strengthen risk assessment and decision-making.\nPartner with technology teams to integrate code and AI-enabled processes into workflows, improving automation and productivity.\nMonitor market developments, particularly in Japanese equities, and assess their potential impact on investments.\nTranslate market trends and quantitative insights into actionable risk-management perspectives.\nParticipate in portfolio manager candidate evaluations and share knowledge and best practices across the global risk management team.\nWhat You Bring\n2 to 10 years of experience in a quantitative role applying analytical skills to financial markets.\nProficiency in Python and SQL.\nA master’s degree or Ph.D. in science, engineering, mathematics or a related quantitative field.\nFluency in Japanese and proficiency in English to collaborate effectively with local and global teams.\nThe ability to communicate complex concepts clearly to a range of audiences.\nA collaborative approach and the ability to thrive in a global team environment.\nExperience with AI and machine learning models is beneficial.\nFamiliarity with equity markets, factor models, performance attribution, portfolio construction and optimization techniques is 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