Microsoft Sr Security Research/AI Engineer 

Taiwan, Taoyuan City 

943674029

Required Qualifications

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research)

• ORMaster's Degreein Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)

• ORDoctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
• ORequivalent experience.

5+ years of programming skills in Python, Java, or similar.

Proven experience (5+ years) working with graph databases and graph analytics frameworks.

5+ years experience with AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn) and graph algorithms.

Experience applying graph techniques in cybersecurity (e.g., threat hunting, anomaly detection, attack path analysis).

Preferred Qualifications

• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

• ORDoctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)

• OR equivalent experience.

• years experiencecreating publications (e.g., patents, libraries, peer-reviewed academic papers).

• Experience presenting at conferences or other events in the outside research/industry community as an invited speaker.

• years experienceconducting research as part of a research program (in academic or industry settings).

• 1+ year(s) experience developing and deploying live production systems, as part of a product team.

• 1+ year(s) experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

Bringing the State of the Art to Products

• expertiseor technology to create business impact. Takes initiative and drives activities such as technology transfers attempts, standards organizations, filing patents, authoring white papers,developingormaintaining

• new technologyand approaches into production by applying long-term research efforts to solve immediate product needs. Collaborates with and bridges the gap between researchers (in community across the company, Microsoft Research [MSR], or in their own organizations) and development teams. Begins to negotiate across teams to ensurecutting edgetechnology is being applied to products in a practical way that meets key businessobjectives. Develops an understanding of research approaches used across a group or organization toleverage(and not re-invent) solutions.

• Identifiesapproach, and applies, improves, or creates a research-backed solution (e.g., novel, data driven, scalable, extendable) to positivelyimpact

Leveraging Applied Research

• one or more subareas (e.g., Object Recognition, Text Classification) and gainsexpertisein a broad area of research (e.g., Machine Learning, Natural Language Processing, Computer Vision, Statistical Modeling, Data-Driven Insights. Understands the corresponding literature and applicable research techniques. Usesexpertisetoidentifythe right technique to use when examining a problem.

• identifyproduct needs and drive action toward solutions. Fosters audience for the product based on understanding of the industry.

• Reviews business and product requirements and incorporatesstate-of-the-artresearch or previously tested solutions occurring at Microsoft and the academic field to formulate plans that will meet business goals.Identifies

• utilized

Capability Management and Networking

• Provides mentorship byparticipatingin onboarding toless experiencedteam members (e.g., interns, research associates) and guidingless experiencedteam members in processes, scenarios, projects, and their careers, and provides guidance around best practices and standards.Assistsin developing academics to be members of multi-discipline

• Identifiesand inspires peers and new research talent to join Microsoft. Participates in candidate screening and interviewing and forms job descriptions for attracting new talent. May share research findings through publications or industry outreach. Collaborates with the academic community to develop the recruiting pipeline,identifycutting-edgesolutions for products, andestablishawareness of their work.

• Performs documentation of work in progress, experimentation results, plans, etc. Documents scientific work to ensure process is captured. Creates informal documentation and may share findings to promote innovation withingroupor with other groups.

• Seekstoidentifypotential bias in the development of

Specialty Responsibilities

• Leverages data analysis knowledge to clean, transform, analyze, integrate, and organize data to the level required by the analysis techniques selected. Developsuseabledatasets for modeling purposes. Scales the feature ideation and data preparation. Takes cleaned or raw data and adaptsdata thatfor machine learning purposes. Uses understanding of which features are important that come out of the model andidentifiestheoptimalfeatures.Identifiesgaps in current datasets and drives onboarding of new datasets. Works with team tooptimizesignal system design. Mentors and coachesless experiencedmembers in data cleaning and analysis best practices.Identifiesgaps in current datasets and drives onboarding of new datasets (e.g., bringing on third-party datasets). Attempts to fix bugs in data to inform developers how to improve the products. Ensures representative data to honor problem definition andethics.

• Establishes the pipeline so that the team can conductall oftheir experiments and data processing.Provides guidance toless experiencedteam members. Uses data pipelines for training, as well as for shipping models which should executecorrectly.

• leveragedata toidentifypockets of opportunity to createstate-of-the-artalgorithms to improve a solution to a business problem. Consistentlyleveragesknowledge of techniques tooptimalanalysis using algorithms.Identifiesopportunity areasregardingnew statistical analyses and drives solutions. Uses statistical analysis tools ormodifiesexisting tools for evaluating Machine Learning models andvalidatesassumptionsabout the data while also reviewing consistency against other sources. Runs basic descriptive, diagnostic, predictive, and prescriptive statistics.Representsthe team's insights. Characterizes the customer's problem through metrics to measure the quality of machine learning systems. Calibrates metrics to support decision making for data (e.g., gaining awareness of ideal metrics and use of metrics).

• Identifiespossible machinelearning formulations that map to the problem and selects the formulation that gives theoptimaloutcome (e.g., predicting the actual age or age group). Leveragesstate-of-the-artalgorithms thatstructures,analyzes, andusesdata in products and platforms to train algorithms scalable for artificial intelligence solutions before deploying. Uses familiarity of machine learning frameworks (e.g., usesopen sourcelibraries) to train algorithms. Collaborates and helpsless experiencedteam members throughprocess.

• Helps address scalability problems by adjusting to stakeholder needs. Works with large-scale computing frameworks, data analysis systems, and modeling environments to improve models. Applies the model to realproducts, andthen verifies effects through iterations. Experiments by putting multiple models in production and evaluating their performance. Mentorsless experiencedteam members through modeling processes. Continues tomonitorhow algorithm performs against expected behaviors and performance or accuracy guardrails. Monitors over time for input and output data that there are changes over time.Uses system to run analyses on an ongoing basis such as by comparing predicted value with actual value.Addresses models that break during production (e.g., due to input streams changing).

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