Machine Learning Engineer, Operations and Health Systems Research

About This Role

Biogen studies highly complex, devastating, and burdensome diseases and there are still significant challenges in how we understand and treat them.

In the Quantitative Sciences Development Organization we aspire to transform patients' lives by accelerating disease research using best in breed and emerging techniques. Leveraging applied machine learning and emerging technologies, we drive solutions to advance research, clinical care, and patient empowerment.

We stand with our colleagues across Biogen developing pioneering treatments. We believe that now, more than ever, biology and technology should go hand-in-hand to better meet patient needs, while enabling a shift towards more prevention-focused, affordable, and equitable care.

What You'll Do

As a Machine Learning Engineer, Operations and Health Systems Research, you will:

• Design, develop, and implement complex end-to-end data pipelines, engineer features and disease models to understand the intersection of health systems utilization and clinical research
• Explore and implement the use of Generative AI models and systems to digest domain specific documents and semi-structured narrative data, harmonize across semantic models and concepts, accelerate analysis, and improve resilience of processes and procedures
• Integrate real world data sources in order to build epidemiological and disease forecasting models used to support decision-making for clinical trial operations
• Generate evidence and communicate results, including at conferences and in peer-reviewed publications for audiences in the therapeutic area of interest, sensor science, machine learning and operations research.
• Support integration, design and development of novel measures, models, and metrics to assess model validity and performance in both training and inference stages

• >= 5 years Pharma/Biotech/Tech/Healthcare industry experience, alternatively extensive experience in academic/clinical centers of excellence
• Expertise in leveraging electronic health records and other data sources derived from clinical care via state of the art methodologies, programming languages, and tools
• Experience working on machine learning and data science problems using formal software development lifecycle approaches and best practices as part of a software delivery organization
• Profound experience in data science, machine learning, and data engineering programming using python/R and higher-level libraries such as numpy, pandas, scipy, statsmodels, scikit-learn, TensorFlow, matplotlib, Plotly, tidyverse ggplot, mlr, tidymodels or similar
• Strong familiarity with large-language models and other encoder-encoder/encoder-decoder architectures, both as served and hosted endpoints and incorporated and fine-tuned for business processes

• Experienced in communicating and building consensus on difficult or sensitive information with technology and business teams
• Experience iterating through UI/UX patterns, data visualization, and front-end interactions and balancing across conflicting stakeholder preferences
• Formal application of principles of empirical science including statistical modeling, design of experiments, and hypothesis formulation and testing
• Advanced degree in public health, epidemiology, public health economics, operations research or a similar discipline

• Medical, Dental, Vision, & Life insurances
• Fitness & Wellness programs including a fitness reimbursement
• Short- and Long-Term Disability insurance
• A minimum of 15 days of paid vacation and an additional end-of-year shutdown time off (Dec 26-Dec 31)
• Up to 12 company paid holidays + 3 paid days off for Personal Significance
• 80 hours of sick time per calendar year
• Paid Maternity and Parental Leave benefit
• 401(k) program participation with company matched contributions
• Employee stock purchase plan
• Tuition reimbursement of up to $10,000 per calendar year
• Employee Resource Groups participation