{"id":1289681,"url":"https://alion.io/job/millennium-management-ai-data-scientist","title":"AI Data Scientist","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":"B","score":75,"open_postings":80,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-28T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","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":["Hong Kong"],"countries":["HK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embeddings","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"Knowledge Graph","optional":true},{"name":"Time Series Forecasting","optional":true}],"status":"live","first_seen_at":"2026-08-13T00:00:00Z","employer_posted_date":"2026-08-13","last_verified_at":"2026-09-28T21:00:45Z","board_verified":true,"closed_at":null,"days_open":46,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":46},"description":"AI Data ScientistABOUT THE TEAM AND ROLE\nThe investment team is building an AI-enabled research and decision platform that brings together proprietary knowledge, public information, market and alternative data, analytical tools and modern machine-learning capabilities.\nWe are seeking an AI Data Scientist to work directly with portfolio managers, investment professionals and engineers. The role will own high-impact projects from problem definition through model development, deployment, evaluation and ongoing improvement. The successful candidate will combine scientific depth with strong engineering judgement and a practical understanding of how data and AI can improve investment research and decision-making.\nPRINCIPAL RESPONSIBILITIES\nTranslate investment and research questions into well-defined data-science problems, measurable objectives and practical technical solutions.\nDevelop and deploy machine-learning, statistical and LLM-enabled models for company research, industry analysis, market monitoring, event detection and knowledge discovery.\nBuild robust workflows across the full data lifecycle, including data sourcing, cleaning, transformation, feature engineering, quality checks, modelling and monitoring.\nDevelop retrieval, search and knowledge systems using structured and unstructured data, with rigorous source attribution and evaluation.\nDesign experiments and evaluation frameworks covering model quality, factual accuracy, robustness, latency, cost and user impact.\nWork with engineers to productionize models and analytical tools through APIs, batch pipelines and monitored applications.\nPartner closely with investment users to understand workflows, communicate trade-offs and iterate based on evidence and feedback.\nIdentify promising models, research and open-source technologies, and determine when they are-or are not-appropriate for real investment use cases.\nImprove tooling, documentation and processes to increase reliability, reduce manual work and enable reuse across the team.\nQUALIFICATIONS / SKILLS REQUIRED\nPhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, Engineering, Physics or another highly quantitative field.\nMinimum 2 years of professional, full-time experience in data science, machine learning, applied AI or a closely related role. Doctoral research alone does not replace the professional-experience requirement.\nStrong Python proficiency and experience with core scientific and machine-learning libraries such as Pandas, NumPy, scikit-learn and PyTorch or equivalent frameworks.\nStrong grounding in machine-learning and statistical fundamentals, including problem framing, experimental design, validation, metrics, feature engineering, overfitting and uncertainty.\nDemonstrated experience delivering at least one end-to-end model or data product used by real stakeholders, from initial scoping through deployment and monitoring.\nPractical experience with LLMs and modern NLP, including retrieval-augmented generation, embeddings, vector search, prompt or context design and systematic evaluation.\nProficiency in SQL and experience working with relational, columnar or document-oriented data systems.\nAbility to work with messy, incomplete and heterogeneous data while maintaining strong standards for data quality, testing, reproducibility and documentation.\nStrong written and verbal communication skills, with professional fluency in English and Mandarin.\nPREFERRED QUALIFICATIONS\nExperience with financial, market, regulatory or alternative datasets, or with research-intensive decision environments.\nExperience building data or AI products in cloud environments and deploying APIs, batch jobs, monitoring or feedback loops.\nFamiliarity with knowledge graphs, document processing, browser automation, data visualization or time-series and event-driven modelling.\nEvidence of technical depth through publications, patents, open-source contributions or substantial production projects.\nGenuine interest in companies, industries and investing; prior investment experience is valued but not required.\nHOW WE WORK\nOwnership: Scope work clearly, set realistic milestones, communicate risks early and follow through on outcomes.\nScientific rigour: Prefer measurable evidence, reproducible analysis and honest uncertainty over impressive demonstrations.\nPractical judgement: Start with the simplest viable approach, use advanced methods where they add value and understand when not to use AI.\nCollaboration: Work closely with investment and technology colleagues, seek feedback and communicate complex ideas clearly.\n Continuous improvement: Track developments in models, research and open-source tooling, and translate relevant advances into durable capabilities.","description_format":"text","description_chars":4768,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"Advanced (C1)","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Hedge Funds & Alternative Investments"],"lifecycle":[{"event":"open","at":"2026-09-26T07:37:06Z"}],"liveness":{"score":5,"band":"cold","label":"Long shot","p_open":1,"p_active":0.175,"p_room":0.28,"age_days":46,"expected_fill_days":17,"reasons":["conf:13","stale_co","velocity","win:tail","crowd:junior,brand"],"computed_at":"2026-09-28T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/millennium-management-ai-data-scientist","json_url":"https://alion.io/job/millennium-management-ai-data-scientist.json","meta":{"generated_at":"2026-09-28T22:10:54Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":219,"day_limit":5000,"remaining_today":4781,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}