Microsoft Senior Data & Applied Scientist 

Taiwan, Taoyuan City 

342712865

As a

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.

• 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.