Machine Learning Engineer, Operations and Health Systems Research

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Position Overview

The Machine Learning Engineer for Operations and Health Systems Research at Biogen will be responsible for developing and implementing advanced machine learning models and algorithms that enhance operational efficiencies and contribute to improved health systems research. The role involves collaborating with cross-functional teams to extract insights from diverse data sources, supporting data-driven decision-making processes, and promoting innovative strategies that align with the organization's objectives. The successful candidate will apply their expertise in machine learning techniques to tackle complex challenges while ensuring the integrity and quality of healthcare data.

Job Responsibilities

• Design and develop machine learning models that optimize operations and inform health systems research.
• Work closely with data scientists and healthcare professionals to identify key performance indicators and metrics.
• Manage the end-to-end machine learning lifecycle, including data acquisition, model training, deployment, and monitoring.
• Perform data preprocessing and feature engineering to enhance model performance.
• Analyze and visualize data to extract actionable insights for various stakeholders.
• Conduct regular audits and validation of existing models to ensure accuracy and reliability.
• Collaborate with IT and data management teams to ensure proper data governance and compliance.
• Stay abreast of the latest developments in machine learning and healthcare technologies to leverage best practices.

Qualifications

• Master’s degree in Computer Science, Data Science, Artificial Intelligence, or related field.
• Proven experience in machine learning, predictive modeling, and statistical analysis.
• Strong programming skills in Python and/or R, with familiarity in machine learning libraries such as TensorFlow or Scikit-learn.
• Experience with data visualization tools (e.g., Tableau, Power BI) and methodologies.
• Knowledge of healthcare data standards and systems (e.g., EMR, claims data) is preferred.
• Excellent analytical and problem-solving skills with a strong attention to detail.
• Ability to communicate complex technical concepts effectively to non-technical stakeholders.

Technologies & Tools

The candidate will utilize a variety of technologies and tools including Python for data manipulation and analysis, machine learning libraries such as TensorFlow and Scikit-learn for model development, SQL databases for data retrieval, and visualization platforms like Tableau for presenting data insights. Familiarity with cloud platforms (e.g., AWS, Azure) is also advantageous for scalable model deployment and data storage.

Ideal Candidates

Ideal candidates for this role will possess a passion for leveraging data to drive improvements in healthcare operations and research. They will be characterized by a strong analytical mindset, exceptional communication skills, and an ability to work collaboratively in a dynamic, team-oriented environment. Additionally, candidates should demonstrate a willingness to embrace innovation, adapt to evolving technologies, and maintain a high degree of professionalism and ethics in all aspects of their work.