Financial Data Scientist Lead
Novartis
Bâle, Canton de Bâle-Ville, Switzerland
vor 12 Tg.
source : Jobeo.ch

Description du poste : Description de poste The Data Scientist Lead is responsible for the capabilities needed to operate a financial and business practice using advanced techniques to understand data and produce insights that are of value to Novartis.

The mission is to solve financial and business challenges applying Advanced Analytics and Data Modelling techniques on a variety of small, medium and big data.

The data scientist lead will drive the development and implementation of advanced forecasting techniques to support key financial processes such as budget, strategic planning, latest outlook.

The data scientist lead will develop methodologies to identify and quantify drivers that impact the financial performances.

The role encompasses the full lifecycle from ideation to requirements elicitation, design, implementation and rollout of the solutions.

It is expected for the Data Scientist Lead to drive proactively the interactions with internal stakeholders across different functions and departments at country, region and global levels, as well as external ones, such as consultants, universities, etc.

Your will be responsible for but not limited to : - Primarily responsible to lead and drive advanced analytics activities in finance, including explorative applications of machine learning, deep learning and artificial intelligence.

  • Excellent quantitative skills and the ability to tell a story using data. - Strong familiarity and experience with data preparation and processing such as assessment of data quality, new variable / features creation, variable / features selection, etc.
  • Strong and wide expertise across a variety of advanced modelling like Time Series, Regression techniques, Artificial Neural Networks, Random Forest, and other supervised and unsupervised learning methodologies, etc.
  • Experiment with new datasets to try unguided and unsupervised learning to understand hidden insights. - Ability to build and fine tune algorithms that scale up from small-
  • scale proof-of-concept stage to full production systems -Build knowledge artifacts of real business problems that were solved with advanced analytics techniques which can be published in journals.

  • Drive Innovation ideas / discussions, PoC's. - Evaluate new technological developments and evolving business requirements and makes recommendations for improved service levels and efficiencies.
  • Project management expertise, especially related to new technologies / innovations. - Actively engage with business and technical stakeholders both internal and external.
  • Structure problem domain and drive / define data, process, functional, and architectural requirements. - Be ultimately accountable for the full lifecycle of the advanced analytics solution.

  • Work with relevant partners in functions or franchises to scope data science requirements : need identification, hypothesis generation, data discovery and methodology proposal.
  • Select and rapidly prototype the models that are appropriate for the problem at hand and the available data that characterizes the components of the problem.
  • Responsible for recommending the most appropriate data science approach to ultimately solve the problem at end.
  • Ensure the selected data science approach can be engineered in an advanced analytics product. - Ensure projects that use external data science consultants or solution vendors are well managed by ongoing engagement.

    Description du profil : Exigences minimales - University degree in Statistics, Operational Research, Computer Science or in a highly quantitative field.

  • Fluency in English 4+ years post-graduate experience in a multidisciplinary data & analytics environment or in research -
  • 2+ years in predictive modeling and large data analysis. - A track record of innovating through machine learning and statistical algorithms and their applications.

  • Hands on experience with data mining, its concepts, techniques and implementation. - Practical experiences with data discovery with large and complex data assets from varied information sources.
  • Experience in statistical / analytics tools such as R, Matlab, Python or big data technologies for machine learning. -
  • Expertise in at least a visualization tools Qlikview, Spotfire, Tableau for designing insights. - Expertise in working with data bases such as SQL.

    Proven self-starter with experience in initiating and ensuring delivery.

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