Lead Data Science Workbench Analyst (Engineer) - Databricks or Snowflake

The Bank sponsors individuals for TN and H-1B transfers on a case by case basis. Please note that this position is not open to anyone on a H-1B or F-1 student visa including those eligible for CPT/OPT or the Stem OPT extension.

This role follows a hybrid work schedule; offering the flexibility to work remotely two days a week, while providing the opportunity for onsite and in person collaboration the other three days.

The Lead Data Science Workbench Analyst takes a strategic leadership role in designing, implementing, and governing enterprise-scale AI/ML infrastructure. This role is responsible for driving automation, platform scalability, and security best practices to ensure that data science teams operate at peak efficiency.

This position requires deep technical expertise in cloud-based data platforms, distributed computing, and workflow automation while also emphasizing cross-functional collaboration with IT, data scientists, and business stakeholders. The Lead Engineer serves as a trusted advisor on AI/ML workbench architecture, data governance, and performance optimization.

Why This Role Matters:

The Lead Data Science Workbench Analyst plays a pivotal role in shaping enterprise AI/ML infrastructure by ensuring that data science platforms are scalable, automated, and highly available. By leading governance efforts, optimizing cloud workloads, and mentoring senior engineers, this role enables AI-driven business success and supports the next generation of machine learning innovations.

Position Responsibilities:

• Architect and lead the deployment of scalable data science platforms across the enterprise.

• Develop and enforce governance policies to ensure security, compliance, and efficient resource allocation.

• Optimize AI/ML infrastructure to support large-scale machine learning workloads.

• Lead automation initiatives to enhance cloud provisioning, monitoring, and infrastructure-as-code (IaC) practices.

• Implement advanced monitoring and observability solutions to proactively manage workbench performance.

• Facilitate cross-functional collaboration between data science, engineering, IT, and security teams.

• Mentor senior engineers in best practices for cloud security, performance tuning, and automation.

• Evaluate and recommend emerging technologies to continuously improve AI/ML workbench capabilities.

• Develop cost management and optimization strategies to ensure cloud efficiency.

Minimum Qualifications Required:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.

• 7+ years of experience managing cloud-based AI/ML platforms and infrastructure.

• Extensive experience with Databricks, and other cloud-based AI/ML workbenches.

• Expert-level proficiency in Python, SQL, and infrastructure automation tools (Terraform, Kubernetes, Docker).

• Proven experience designing and implementing large-scale data science platforms.

• Strong knowledge of security best practices, IAM policies, and compliance frameworks.

• Experience with CI/CD pipelines and DevOps methodologies for AI/ML workloads.

Nice to Have (Preferred Qualifications):

• Certifications: AWS Certified DevOps Engineer, Databricks Advanced Architect, Snowflake Advanced Data Engineer.

• Experience optimizing high-performance computing (HPC) environments for AI/ML workloads.

• Proficiency in real-time data streaming technologies (Kafka, Spark Streaming, Kinesis).

• Knowledge of AI model lifecycle management tools such as MLflow or SageMaker.

• Strong background in cost management and optimization (FinOps) for cloud resources.

M&T Bank is committed to fair, competitive, and market-informed pay for our employees. The pay range for this position is $127,274.43 - $212,124.05 Annual (USD). The successful candidate’s particular combination of knowledge, skills, and experience will inform their specific compensation.

LocationWilmington, Delaware, United States of America