Data Science II

Are you excited to tackle data science problems in security, privacy, and compliance domains? Are you a full-stack data scientist, adept at collaborating with stakeholders to define, prototype, implement, and deploy end-to-end data science solutions? If so, then the Azure Core Trusted Platform team is a fit for you! Our team is committed to enabling Azure product teams to deliver the most secure, private, safe, reliable, and compliant cloud platform.

As a Data Scientist II in the Azure Core Trusted Platform team, you will apply ML modeling, AI technologies, and data analysis to create products and generate insights that are widely used across Microsoft. You will leverage the latest technologies, such as large language models and generative AI, to solve problems like identifying personal data in telemetry logs, detecting signatures of malicious activity on the Azure platform, and ensuring all production code is compliant with the latest security standards. You will take ownership of projects end-to-end, being deeply involved in all phases of a data science lifecycle: problem definition and scoping, experimentation and prototyping, implementation and deployment, and feedback and monitoring. You will succeed in this role by demonstrating the ability to solve problems creatively, collaborate with cross-functional stakeholders, deliver solutions with technical excellence, and iterate quickly with a fail-fast mindset. Our team supports hybrid work and fully remote work. Join our team and help make Azure the most trusted cloud platform!

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities

• Business understanding: Understands underlying business and product goals to inform design of data science solutions. Evaluates project plan for resources, risks, contingencies, requirements, assumptions, and constraints. Effectively communicates business goals, data insights, and data science solutions with variety of stakeholders.
• Data fluency: Explores, queries, visualizes, and processes data sets to deeply understand available datasets. Evaluates and leverages existing methodologies and tools, such as statistical and ML packages. Able to identify and propose solutions to data integrity and quality issues.
• Modeling & analysis: Understands pros/cons of wide variety of ML techniques (classification, regression, clustering, time series analysis, natural language processing, etc.) and algorithms (linear/logistic regression, gradient boosting, agglomerative clustering, deep neural networks, Transformer networks, etc.) to select the most appropriate solution. Applies standard modeling techniques (cross-validation, regularization, ensembling, etc.) as appropriate to ensure quality, reproducible results. Conducts well-designed experiments, performs statistically sound analyses, communicates results clearly and accurately to stakeholders (engineering and PM teams and leadership).
• Measurement & iteration: Measures success of data science solutions in the context of business impact and goals. Analyzes model performance and quickly iterates to improve performance metrics.
• Engineering skills: Writes efficient, scalable, maintainable code. Performs comprehensive quality checks for data processing and ML modeling code. Understands proper debugging techniques when dealing with data pipelines and non-deterministic code. Familiar with big data tooling, ETL pipeline principles, REST API consumption and deployment.
• Customer focus: Considers user experience when designing data science solutions. Examines and evaluates projects through customer-focused lens. Responsive to user feedback.
• Embody our Culture and Values 

Qualifications

Required Qualifications: 

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
  • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experie