bluebird bio is a company that puts patients and its people first.
We are a clinical-stage company committed to developing potentially transformative gene therapies for severe genetic diseases and T cell-based immunotherapies. The company aims to develop and bring to market the most advanced products based on the transformative potential of cell and gene therapy to provide patients hope for a better life in the face of limited or no long-term safe and effective treatment options.
We have been consistently voted one of Boston Business Journal’s “Best Places to Work”. You can expect to join a team of motivated, inquisitive scientists that recognize the value of working hard, but also do not take themselves too seriously.
The ML data scientist will join our data sciences team that uses advanced analytics and machine learning on clinical, molecular, manufacturing and other data to support bluebird’s mission to recode for life. You will be part of a dynamic team and have the opportunity to bring robust and insightful data analytics across the organization.
Apply cutting-edge machine learning methods to solve complex, biomedical questions by integrating disparate data types including omics, clinical, imaging, text and RWE for specific business applications.
Use statistical methods, including inference, causal reasoning, NLP, machine learning and deep learning, to drive actionable insights or generate testable hypotheses.
Be an advocate for driving awareness, acceptance, and adoption of advanced analytics across the organization.
Communicate with stakeholders and senior management to iteratively refine the problem definition and to deliver results.
Interact with functional areas including IT to find new avenues for collaboration.
PhD in computer science, biostatistics, computational biology or related quantitative field.
Required 2+ years industry or research experience in applying ML and/or statistical inference methods to high-dimensional datasets and communicating results to diverse audiences.
Expertise with R or Python data science libraries, SQL, Spark, code versioning, programmatic data visualization. Knowledge of deep learning frameworks is a plus.
Domain knowledge in one or more of the following is required: genomics (NGS), NLP, imaging (pathology or medical), real world EHR or epidemiological data, computational biology or immuno-oncology or severe genetic diseases.
Experience working on complex and dynamic projects, making decisions under uncertainty and dealing with shifting priorities.
Deep curiosity about emerging trends in AI/ML and molecular technologies, passion for tackling cancer and severe genetic disorders.
Follow bluebird bio’s core values: b colorful, b co-operative, b yourself.