微软Senior Data & Applied Scientist

任职要求

Required Qualifications:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)• OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techn
• OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical tec
• OR equivalent experience.

• 2+ years customer-facing, project-delivery experience, professional services, and/or consulting experience
• 4+ years applied ML/NLP experience delivering models and features to production at scale. 
• Software engineering excellence:• Proficiency in Python and PyTorch (or equivalent DL framework). 
• Solid SDLC practices: unit/integration testing, CI/CD, code reviews, version control, performance profiling, and reliability hardening. 
• Ability to write clean, maintainable, efficient code for production services and clients. 

• Experimentation & evaluation: sound experimental design, metric design (quality, safety, latency, cost), and statistical analysis; experience running online A/B tests. 
• Proven collaboration with PM & Engineering to integrate ML into shipped product (APIs/services/clients) and to drive measurable user or business impact.
Preferred Qualifications:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science,• OR related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science,
• OR related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science,
• OR related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• OR equivalent experience.

• Graduate degree (MS/PhD) in ML/AI or related field (or equivalent applied research impact). 
• Depth in transformers/LLMs (pretraining, SFT, alignment/RLHF/DPO), RAG, prompt/agent tooling, and safety/abuse mitigation for generative systems. 
• Production ML engineering at scale:• Model serving/inference (e.g., ONNX Runtime, vLLM, Triton, quantization, distillation, caching, dynamic batching, rate limiting). 
• Distributed training (PyTorch Distributed, DeepSpeed, FSDP), mixed precision, checkpointing, data‑pipeline performance (Parquet/Arrow). 
• Service development: stable APIs/SDKs, microservices, feature flags, safe rollouts/rollbacks, config & traffic ramps. 
• Observability & live‑site: SLIs/SLOs, dashboards, structured logging, tracing, alerting, on‑call, and postmortems. 
• Experimentation: A/B & interleavings, guardrail metrics (quality/safety/latency/cost), sequential testing, eval governance. 
• Data engineering: ETL at scale (Spark/Databricks), feature stores, vector indexing (Azure AI Search/FAISS/Milvus), data quality checks. 
• Cloud & orchestration: Azure ML, AKS/Kubernetes, containerization, autoscaling, artifact & secret management, policy enforcement. 
• Security & privacy: data minimization, access controls, audit logging in enterprise SaaS contexts.   

Other Requirements:

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

 

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

工作职责

• Ship features with PM & Engineering. Co‑own scenario goals; translate product requirements into scientific plans and productionized solutions that meet quality/latency/cost targets.
• Model development & optimization. Design, fine‑tune, and evaluate models for LLM‑based authoring, summarization, reasoning, voice/chat, and personalization (e.g., SFT, alignment, prompt/tool use, safety filtering, multilingual & multimodal).
• Data & evaluation at scale. Build/extend data pipelines for curation/labeling/feature stores; author offline eval harnesses; run online A/Bs and interleavings; define guardrails and success metrics; author scorecards and decision memos.
• Production ML engineering. contribute to service code and configs; add monitoring, tracing, dashboards, and auto‑scaling; participate in on‑call and postmortems to improve live‑site reliability.
• Responsible AI. Produce review artifacts, document mitigations for safety/privacy/fairness, support red‑teaming and sensitive‑use checks, and align with Microsoft’s Responsible AI Standard.
• Collaboration & mentoring. Partner across PM/ENG/Design/CE/ORA/CELA; share methods and code, review PRs, improve reproducibility and documentation; mentor junior scientists.