Senior Manager, Analytical AI - Engineering

The Senior Manager, Analytical AI at Bristol Myers Squibb plays a critical role by leading a team of data engineers and machine learning engineers. Their responsibilities include designing and maintaining data pipelines, deploying machine learning solutions, and ensuring their scalability and reliability. Collaboration is key, as they will work closely with analytics and business stakeholders to create innovative data-driven solutions that make a difference in patient care.

Candidates for the Senior Manager, Analytical AI position at Bristol Myers Squibb typically need at least a BA/BS in a quantitative field, with preferred advanced degrees such as an MBA or MS. Relevant experience is crucial, requiring a minimum of 3 years in data science or machine learning, with proficiency in technologies like Python, SQL, and cloud-based platforms. Strong communication skills for explaining complex data analyses are also vital.

The Senior Manager, Analytical AI at Bristol Myers Squibb directly contributes to the company's mission of transforming patients’ lives through science. By leveraging analytics and machine learning, they facilitate the development of innovative solutions that refine patient care and drive clinical capabilities, underscoring the essential role data plays in modern medicine.

In the role of Senior Manager, Analytical AI at Bristol Myers Squibb, you'll engage with a variety of tools and frameworks. Key technologies include Python, R, SQL, as well as machine learning frameworks like Scikit-Learn, TensorFlow, and PyTorch. Additionally, you'll use cloud platforms like AWS and Azure and may work with applications built in Dash, Streamlit, or R Shiny to enhance data science initiatives.

As a Senior Manager, Analytical AI at Bristol Myers Squibb, you can expect a collaborative and inclusive work environment, emphasizing flexibility and balance. The company supports hybrid work models, allowing for a blend of onsite and remote work. With a commitment to diversity and innovation, Bristol Myers Squibb fosters an atmosphere where each team member's contributions are valued in the pursuit of their mission.

When designing machine learning pipelines, it’s essential to first understand the business problem and the data available. I focus on building scalable and reliable frameworks, utilizing best practices in MLOps to ensure smooth deployment. Collaboration with data scientists is paramount to ensure that the pipeline meets analytical needs and supports iterative improvements over time.

My experience with cloud environments includes deploying machine learning models and managing data storage solutions on AWS. I appreciate the flexibility these platforms offer in terms of scaling resources and conducting analytics efficiently. I have also used Azure for data engineering, utilizing its machine learning services to streamline processes and drive collaboration among teams.

To effectively communicate complex data analyses, I tailor my message based on the audience's expertise. I use data visualizations to simplify intricate concepts and focus on key insights rather than data overload. Encouraging questions fosters an interactive dialogue, helping bridge the gap between technical and non-technical stakeholders.

I prioritize tasks by assessing their impact on overall project goals and timelines. I utilize project management tools to organize and track progress while ensuring effective communication with team members. Regular check-ins and updates help maintain focus on critical priorities and address any potential roadblocks proactively.

A common challenge in deploying machine learning solutions is ensuring model accuracy and reliability in real-world scenarios. I address this by implementing robust testing and validation protocols during the development stage. Additionally, I collaborate with cross-functional teams to gather feedback and iterate on solutions, which helps fine-tune our approach and improve outcomes.

I prefer frameworks like TensorFlow and PyTorch for their robust ecosystems and active community support. TensorFlow is great for production-ready applications due to its scalability, while PyTorch is favored for its flexibility in research and development. Choosing the right framework often depends on the project requirements and the team's familiarity.

My experience with data governance emphasizes ensuring compliance with industry standards and best practices. This involves implementing guidelines for data access, usage, and security. I work closely with compliance departments to ensure our analytics practices uphold regulatory standards, especially in sensitive environments like healthcare.

I stay informed about advancements in data science and AI by engaging with industry publications, attending conferences, and participating in online courses. Following influential researchers and practitioners on platforms like LinkedIn helps me gain insights into emerging trends, allowing me to integrate innovative practices into my work.

In a previous role, I led a cross-functional project where we developed an AI tool to analyze patient data. By coordinating between data scientists, IT, and business stakeholders, we established a clear roadmap and successfully delivered the project ahead of schedule. Regular updates and collaborative discussions kept everyone aligned and motivated.

Team collaboration is fundamental to my role as a Senior Manager. I believe that diverse perspectives enrich solutions, so I foster an inclusive atmosphere that encourages teamwork. Regular brainstorming sessions and open lines of communication allow us to leverage each member's strengths, ultimately resulting in more robust and innovative outcomes.