Job id :  9BEDC428-2FB5-4


Responsibilities & Qualifications


The Intern, will support the Data Science and Advanced Analytics team in providing data-driven answers to complex strategic challenges. The scope of such challenges may span every business line and every geographic location within TE Connectivity. This role will perform analysis and synthesis along the insight generation continuum, from strategy formation through presentation.


In addition, the Intern will participate in continuous process improvement activities, help in managing projects, and execute ad-hoc requests from the Global Data Science and Advanced Analytics leadership team.


Daily work may involve performing one or more of the following activities:

•     Translating a complex strategic challenge into relevant analytic framework(s).

•     Participate in meetings to gather business requirements using interviews and questionnaire.

•     Ask probing questions to understand business intent and how the end-results will be used. Surface implicit assumptions by the business user. Agree on template end-deliverables.

•     Support due diligence efforts to ensure data is obtainable and timelines are feasible for the analysis required.

•     Create project management plans defining the analytical projects’ scope and timeline.


Gathering, cleaning and structuring existing data:

•     Where required, pull data into analytic environment using Oracle, SQL, Hadoop, Access or Excel.

•     Where relevant, clean and structure data to facilitate analysis.

•     Identify and catalog strengths and weakness of respective data sources, noting areas of questionable data that could sidetrack analytic output.

•     Perform exploratory data analyses.

Applying appropriate decision technology tools, algorithms and methodologies:

•     Identify appropriate decision technology techniques to apply to relevant analytic frameworks. Examples of decision technology tools that may be used include optimization, simulation, regression, decision trees, neural networks, cluster analysis, mixed models, etc.

•     Set up model and conduct analyses using R, Python and other data science frameworks

•     Write custom code as required.

•     Document steps in the analytic process. Ensure models are easily understandable and maintainable.

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