Sr Engr Cslt-Data Science

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Sr Engr Cslt - Data Science

Responsible for leading the design and implementation of processes and layouts for complex large-scale data sets used for modeling data mining and research purposes. Leads the design and build of large and complex data sets from spurious sources while thinking strategically about uses of data and how data use interacts with data design. Works with key stakeholders to generate hypotheses and create analytic models that answer impactful business questions. Manages a small team of employees including planning work and dividing it among employees.

Responsible for the companys technology roadmap and strategy. This includes the acquiring building designing implementing securing maintaining and operating of the information systems (internally and externally) platform architecture network infrastructure and network services. Responsibilities may include both physical and virtual services.

Build design and manage enterprise database systems and/or large relational databases. Integrate new systems with existing warehouse structure and refine system performance and functionality. Develop tools and/or analyses used to interpret large volumes of transactional or other forms of data.

Designs and implements processes and layouts for complex large-scale data sets used for modeling data mining and research purposes. Designs and builds large and complex data sets from spurious sources while thinking strategically about uses of data and how data use interacts with data design. Works with key stakeholders to generate hypotheses and create analytic models that answer impactful business questions.

• Manage the design and implementation of business data models providing support and guidance to their team.

• Collaborate with the internal teams and peers to determine the needs of the business and execute on the development of data models and solutions.

• Determine the strategy of their team and manage the execution driving the standards and principles that govern model development validation and implementation.

• Drive continuous improvement leveraging the latest industry knowledge and methods on AI and data modeling.

• Ensure the organization meets the metrics set-forth by leadership including administrative auditory and KPIs.