Open Position: Data scientist / Data analyst

The Paris Transplant Group in Paris, France, is seeking a data scientist/analyst in the field of solid organ transplantation.
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The data scientist/analyst is a resource who makes value out of data. Such a person proactively fetches information from various sources and analyzes it for better understanding about how the clinical activity and patient’s follow-up perform, and to build AI tools that automate certain processes regarding data treatment. He/she also who subscribes to the vision of the importance of scientific thinking and is keen to apply it to real-world challenges.

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His/her duties will include creating various machine learning-based tools or processes within the team, such as recommendation engines or automated lead scoring systems. The resource within this role should also be able to perform statistical analysis. You are detail-oriented, meticulous, and an analytical team player. You ask the right questions, effectively communicate across teams, and want an opportunity to help shape what data can offer to our knowledge of organ transplantation.

Responsibilities

• Conducting rigorous scientific research by gathering, analyzing, and synthesizing existing scientific research, including insights drawn from our own databases as well as the scientific literature. 
• Critically evaluating existing literature to identify how the research can be applied to the field of organ transplantation, as well as identify any research gaps (i.e., what has not been studied before).
• Selecting features, building and optimizing classifiers using machine learning techniques
• Data mining using state-of-the-art methods
• Extending the group’s data with third party sources of information when needed
• Enhancing data collection procedures to include information that is relevant for building analytic systems
• Processing, cleansing, sorting and verifying the integrity of data used for analysis
• Assisting/doing ad-hoc analysis and presenting results in a clear manner
• Interpreting and translating results from experiment outputs
• Investigating and learning new analytic methods that can be applied to experimental and clinical data
• Creating automated anomaly detection systems and constant tracking of its performanc

Technical Skills and Qualifications

• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
• Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, SAS, SPSS etc.
• Experience with data visualisation tools, such as D3.js, GGplot, etc.
• Proficiency in using query languages such as SQL, Hive, Pig etc.
• Experience with NoSQL databases, such as MongoDB, ElaticCassandra, HBase etc.
• Very good applied statistics skills, such as distributions, statistical testing, regression, etc.
• Very good scripting and programming skills
• Strong Data-oriented personality

Writing and communicating scientific ideas and summarizing experiments in a clear and accessible way

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