Job ID: 217075BR
Job date: 2017-07-24
End Date:
Company : Novartis Country : Role : Research Scientist
Job date: 2017-07-24
End Date:
Company : Novartis Country : Role : Research Scientist
Job Description:
Drives the Translational Medicine (TM) preclinical, clinical, biomarker and Omics data integration. Interacts with TM scientists and management to analyse needs and proactively propose solutions. Drives cross functional teams consisting of TM, NX and external partners like Contract Research Organizations to ensure that adequate solutions are implemented. Analyze the Translational Medicine data landscape, conduct gap analysis and design ways to integrate data to speed up its analysis and the project delivery. Create new capacities for Translational Medicine teams by easing access to data, relevant algorithms, and visualizations as part of the Novartis Digital Strategy.
Additional Info:
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Drives the Translational Medicine (TM) preclinical, clinical, biomarker and Omics data integration. Interacts with TM scientists and management to analyse needs and proactively propose solutions. Drives cross functional teams consisting of TM, NX and external partners like Contract Research Organizations to ensure that adequate solutions are implemented. Analyze the Translational Medicine data landscape, conduct gap analysis and design ways to integrate data to speed up its analysis and the project delivery. Create new capacities for Translational Medicine teams by easing access to data, relevant algorithms, and visualizations as part of the Novartis Digital Strategy.
- Lead process optimization effort, help consolidate existing and new data flows (data ingestion, integration and analysis) to enable data-centric questions. Make real time availability of data a reality for Translational Scientists. Eliminate bottlenecks like manual interventions and data reconciliations.
- Implement an infrastructure that enables the Translational Medicine real-time/digital device vision.
- Lead development of textual and graphical representations of the data in close collaboration with data scientists. Create visualization POCs with selected scientists before releasing it to the NIBR Translational Medicine community.
- Lead master data management activities like ontology construction and data cleaning at the NIBR and Novartis level to increase data inter-operability.
- Be a liaison between partners involved: scientific teams having data intensive needs, internal and external partners producing data, NIBR teams designing enterprise solutions, “data rich” groups like computational sciences groups, statistics, pharmacometrics, pharmacovigilance and Real World Evidences teams.
- Maintain expertise in best practices for data handling especially as it applies to “big data” (data warehousing, data integration, archiving, scalable analysis solutions etc.)
- Data integration. Experience integrating data from different sources is a must. Knowledge of key concepts of Master Data Management (MDM) is a must. Knowledge about relevant data standards (ex: CDISC SDTM), code lists/ontologies (ex: MedDRA, Snomed) use and maintenance is strong plus.
- Vizualisation. Graphical representation of data is one of the necessary endpoints to empower teams.
- Programming. To be able to progress projects, discussion with IT professionals and own manipulation of data are both essential. They are enabled by knowledge of programming/scripting/coding.
- Strong communications skills; Ability to represent the group within NIBR and throughout the wider scientific community; Good organizational and project management skills; Ability to work under pressure and meet timelines; Ability to work as part of an international team in a matrix organization.
- Data bases. Basic knowledge of data access technologies including SQL is required.
- Knowledge of Translational Medicine questions and related data is a plus.
- Statistics. Basic skills in exploratory statistics are required to enable relation to data analysts and statisticians. This includes the understanding of hypothesis testing, multivariate modelling techniques including fixed/mixed effects linear models, different machine learning methods, time course analyses, cross-validation and re-sampling methods. Basics of data mining (graphical, numeric and network-based methods) is also a plus.
- Competitive Intelligence. Keeping up to date with external trends is important. Attending relevant conferences and working group is part of the role.
- Biology. Knowledge and experience in molecular biology, genetics, biochemistry and cell biology is a strong plus to enable support of TM labs and projects.
Requeriments :
Skills :
- Biochemistry
- Biology
- Cell Biology
- Data Mining
- Genetics
- Machine Learning
- Modeling
- molecular biology
- Programing Skills
- SQL
- Statistics
Areas :
Additional Info:
[Click Here to Access the Original Job Post]