Data Scientist

Who You’ll Work With

The Outshift team is highly visible within Cisco as it's leading the future innovation for the business. As part of the Strategy, Incubations & Applications group led by Cisco’s Chief Strategy Officer, you’ll be working with some of the organization’s most forward thinkers.

In this role, you can expect to work with engineering, design, and scientists in defining and executing the product plan.

Conduct customer, market, competitive, and industry research. Additionally, have a proven grasp on overall landscape forgenerative AI machine learning models, tooling, and/or applications, and develop a clear strategy around how to elevate competitive differentiators.

Own the creation of Minimal Viable Product (MVP) definition and then defining a product roadmap that elevates the competitive differentiators at every stage. Define customer value prop and return on investment at each stage.

Work with marketing and business development teams in identifying and executing effective Go-To-Market (GTM) strategies for your product. Establish Key Performance Indicators (KPIs) and metrics to track progress.

Key metrics & measures – Establishing milestones & metrics to evaluate the success and failure of the project. Also, pay close attention to metrics and measures important to customer stakeholders and establish a compelling Return On Investment (ROI) for them.

Collaborate with marketing team in identifying key collaterals – product documents, data sheets, value props, sales decks, ROI calculators, demo videos (it is especially important for the product manager to own demo scripts and video during MVP and initial stages of the product life cycle).

What You’ll Do

In this role, as a Data Scientist, you will understand the business problems and challenges, lead identification, analysis for trends and integrations of large structure and unstructured datasets, build data pipelining, clean/process/transform data sets, highlight insights which that can be leveraged by products. You will devise and utilize algorithms and models to mine big-data stores; perform data and error analysis to improve models; clean and validate data for uniformity and accuracy. Collaborate with software developers and machine learning engineers and follow data science methodologies around model selection, training data set sampling strategy, model validation and monitoring to ensure quality and performance of models in production.