Sr. Manager, Analytics Operations

Working with Us
Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible.

Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us.

The Global Product Development and Supply (GPS) Analytics & AI Enablement (A&AIE) is part of the Business Insights and Technology (BIT) and focuses on enabling analytics and AI capabilities for GPS stakeholders. The GPS organization at Bristol Myers Squibb supports drug development from discovery through commercialization and distribution to patients. Their scope includes portfolio strategy, manufacturing and distribution, network strategy, brand analytics, drug substance, drug product, and analytical development for small molecules, biologics, and cell therapy assets.

Job Description

As an Analytics Operations - Senior Manager at BMS, you will play a pivotal role in implementing, and managing our Analytics Assets Life Cycle. You will lead efforts to deploy, monitor, and automate analytics assets in production, ensuring their reliability, scalability, and performance. You will work closely with data scientists, software engineers, and IT teams to streamline the end-to-end model lifecycle.

Key Responsibilities

• Ensure that analytics assets (data models, ML, AI, and DI) are reliably and efficiently deployed and maintained in production.
• Set up continuous integration pipelines to automate the testing, validation, and deployment of analytics assets.
• Implement automated monitoring and alerting systems to ensure that any disruptions or anomalies in the analytics assets are promptly addressed.
• Collaborate with data engineers and scientists to understand data and model requirements and ensure seamless integration into production systems.
• Troubleshoot and resolve complex issues related to deployment, performance, and scalability.
• Develop governance policies for data management, model development, and analytics deployment. Scale best practices across the organization to ensure consistency and efficiency
• Implement robust security measures to protect data assets. Participate in decision making and brings a variety of strong views and perspective to achieve team objectives. ​
• Demonstrate a focus on improving processes, structures and knowledge within the team. Lead in analyzing current states, deliver strong recommendations in understanding complexity in the environment, and the ability to execute to bring complex solutions to completion.​
• Stay up-to-date with the latest trends and advancements in MLOps and machine learning technologies..

Qualifications & Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• 5+  years of experience in deploying and managing models in production environments.
• Lead initiatives related to continuous improvement or implementation of MLOps.
• Works independently. Responsible for the direct management of a cross functional team including results/outcomes
• Strong understanding of machine learning principles and model lifecycle management.
• Strong programming skills in languages such as Python, PySpark, SQL, Bash/PowerShell
• Extensive experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Proficiency in cloud platforms (e.g., AWS is preferrable, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes).
• Expertise in CI/CD tools (e.g., Jenkins, GitLab CI, CircleCI) and version control systems (e.g., Git).
• In-depth knowledge of monitoring and logging tools (e.g., CloudWatch, Grafana, ELK stack).
• Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment.
• Excellent communication skills and the ability to articulate and present complex information clearly and co