Jr. Data Scientist

1.Collection of requirements and/or needs

2.Definition of the high-level scope/purpose of algorithm and application

3.Identification of the proper analytics framework to achieve the above-mentioned objectives

4.Algorithms/analytic development within assigned projects taking care of the technical standpoint, using sound engineering principles and adhering to standards, best practices, procedures, state-of-the-art technologies (including ML/AI) as well as product/program requirements. In detail:

Collection of detailed requirements with all the necessary information and relevant updates into the predefined projects repository

Risk analysis and revision of required process steps according to the risk level for the analytics in development within the project. Ensure mitigation action are in place if needed, comply with foreseen reviews

Conceptual design and design concept review evaluating the level to which product requirements will be met with the proposed design

Release plan definition and project documentation list preparation

Software development process in incremental and rapid cycles

Intermediate results review to assess design concept, technical risk and verify that product requirements will be met

Analytics validation at fleet level as per final release acceptance criteria

Final review to analyze residual risk and prove adherence to product requirements

Verification, validation, and final implementation of analytics

Analytics Release/Deploy in production environment

Handover to customers

5.Project reviews attendance and report out preparation during project phases

6.Preparation of technical documentation consistent with engineering policies and procedures using companies repository

Requirements understanding analytical problem formulation data preparation algorithm/model development validation deployment support documentation stakeholder collaboration

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