Senior Data Scientist

Sanofi Aventis   •  

Cambridge, MA

Less than 5 years

Posted 278 days ago

This job is no longer available.

Reporting to the Head of Data Science and Engineering, this role will help find solutions to complex analytical problems spanning all divisions of Sanofi. The role will engage across a variety of health and life science problem areas including computational biology, population health, behavioral economics, natural language processing, sentiment analysis, computer vision, cloud computing, and others. Senior Data Scientists are expected to bring some level of domain expertise in a relevant area but must also be committed to engagement with a diverse set of scientists and domain experts across traditional disciplinary boundaries.

Key responsibilities: 

  • Work with domain experts to understand, research, and produce solutions to complex analytical problems.
  • Work with engineers to design, code, train, test, and iterate on large scale machine learning and analysis systems.
  • Work with other stakeholders to translate analytical insights into meaningful decisions, actions, or automated systems as required
  • Help shape the company-wide data science culture across Sanofi.

Basic Qualifications:

  • BS and MS in Computer Science, Computational Biology, Biostatistics, Cognitive Science or similar field with a quantitative research component
  • 3 years experience performing world-class quantitative research in an academic or industry setting with a corresponding publication record
  • Strong analytical and problem-solving skills
  • Software engineering skills across multiple languages such as Python, R, SQL, Java, Scala
  • Experience with popular analytical tools such as Pandas, Scipy, Scikit-learn, Tensorflow, Jupyter, matplotlib, ggplot2, or SparkML


  • Ph.D. in Computer Science, Computational Biology, Cognitive Science or related quantitative field
  • Extensive experience with standard statistical analysis and machine learning techniques
  • Extensive experience with standard software engineering tools and methodologies
  • Deep knowledge of computer science concepts pertaining to algorithmic complexity and distributed computing
  • Experience with scalable analysis tools and platforms such as Hadoop, Spark, AWS, or GCP