Data Scientist

Microsoft

Feb 10

Applications are closed

Job
Full-time
Mid Level
Data
Software Engineering

Requirements

4+ years of experience in machine learning, MLOps/AIOPs, or software engineering roles.
Proven track record of deploying large-scale machine learning systems in production.
Strong experience with cloud platforms (Azure preferred) and infrastructure as code (e.g., Terraform, ARM templates).
Advanced knowledge of MLOps/AIOPs practices, including pipeline automation, monitoring, and orchestration.
Experience optimizing ML models for performance and scalability in production environments.
Demonstrated ability to lead initiatives, mentor junior team members, and influence cross-functional teams.
Solid understanding of security and compliance frameworks relevant to ML operations.
Hands-on experience in building and deploying ML models in a cloud environment (preferably Azure).
Proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch).
Experience with containerization (Docker, Kubernetes) and microservices architecture.
Strong knowledge of CI/CD tools and workflows (Azure DevOps, GitHub Actions).
Basic understanding of model monitoring, retraining, and model governance practices.
Experience with Azure Machine Learning, Azure Fabric, Synapse, or similar platforms.
Strong understanding of data versioning, governance, and reproducibility in ML workflows.
Knowledge of responsible AI practices, including fairness, transparency, and bias mitigation.
Strong communication skills and the ability to work in a fast-paced, collaborative environment.

Responsibilities

Collaborate with data scientists and engineers to design, build, and deploy machine learning models at scale.
Develop and maintain MLOps/AIOPs pipelines to automate the end-to-end lifecycle of machine learning models (from development to deployment, monitoring, and retraining).
Work on the integration of models into production systems while ensuring scalability, security, and performance.
Implement CI/CD pipelines for ML models, ensuring smooth deployments with minimal downtime.
Design and deploy robust monitoring and alerting systems for ML models in production to detect issues such as model drift or data skew.
Implement model governance, version control, and logging systems to ensure compliance with internal standards and external regulations.
Optimize machine learning models and pipelines for performance and cost efficiency (compute, storage).
Manage infrastructure for ML workloads using cloud-native tools (Azure, Kubernetes, Docker) or other container orchestration platforms.
Partner with cross-functional teams, including Data Engineering, Product Management, and other Engineering teams to build cohesive solutions.
Provide technical guidance to junior engineers and drive best practices for MLOps/AIOPS within the team.
Work on securing models, data pipelines, and infrastructure in compliance with Microsoft's security standards.
Ensure that the entire ML lifecycle adheres to privacy and compliance requirements (e.g., GDPR, CCPA).

FAQs

What is the role of a Data Scientist in the Customer Zero Engineering team?

The Data Scientist will be responsible for developing, deploying, and operationalizing machine learning models at scale, collaborating closely with data scientists, software engineers, and product teams to ensure the models are secure, reliable, and performant.

What are the required qualifications for this position?

The ideal candidate should have 4+ years of experience in machine learning, MLOps/AIOPs, or software engineering roles, a proven track record of deploying large-scale machine learning systems, and strong experience with cloud platforms (preferably Azure).

What programming languages and frameworks should applicants be proficient in?

Applicants should be proficient in Python and have experience with machine learning frameworks such as TensorFlow or PyTorch.

What tools and technologies are emphasized for this role?

The role emphasizes experience with cloud platforms (Azure preferred), containerization tools (Docker, Kubernetes), CI/CD tools (Azure DevOps, GitHub Actions), and infrastructure as code tools like Terraform or ARM templates.

Is experience with model governance and compliance necessary for this role?

Yes, a solid understanding of security and compliance frameworks relevant to ML operations is required, along with experience in implementing model governance practices.

Will there be opportunities for mentorship in this position?

Yes, the role involves providing technical guidance to junior engineers and driving best practices for MLOps/AIOps within the team.

What type of environment does the Business & Industry Copilots group provide?

The group offers an agile, high-energy environment focused on innovation and collaboration with partners and customers.

Are there opportunities for professional growth within this role?

Yes, the role is designed for collaboration with cross-functional teams and provides opportunities to lead initiatives and mentor team members, fostering professional growth.

Is prior experience with Azure Machine Learning or similar platforms beneficial?

Yes, experience with Azure Machine Learning, Azure Fabric, Synapse, or similar platforms is desired and can enhance a candidate's application.

What is Microsoft's mission related to this role?

Microsoft’s mission is to empower every person and every organization on the planet to achieve more, fostering a culture of inclusion where everyone can thrive at work and beyond.

Microsoft

Technology

Industry

10,001+

Employees

1975

Founded Year

Mission & Purpose

Every company has a mission. What's ours? To empower every person and every organization to achieve more. We believe technology can and should be a force for good and that meaningful innovation contributes to a brighter world in the future and today. Our culture doesn’t just encourage curiosity; it embraces it. Each day we make progress together by showing up as our authentic selves. We show up with a learn-it-all mentality. We show up cheering on others, knowing their success doesn't diminish our own. We show up every day open to learning our own biases, changing our behavior, and inviting in differences. When we show up, we achieve more together. Microsoft operates in 190 countries and is made up of more than 220,000 passionate employees worldwide.