Data Office Data Scientist – Undergrad Intern At Dell Technologies
Full Job Description
Data Scientist – Undergraduate Intern
The Office of the CTO at Dell Technologies is responsible for predicting future technology trends and recommending investment strategies based on those trends. The Data Office team uses leading technologies in order to use data to predict future technology trends.
You will be involved in assessing the competition, developing technology and products, and generating intellectual property. As an Undergrad Intern Data Scientist at the Data Office, you will join a global team of data scientists, developers, researchers, and technologists in researching and applying the latest ML and DL technologies, leveraging the Data power in adding innovation value to business strategic decisions.
What you’ll achieve
As an Undergraduate Intern, you will work with a cross-functional global team to research, build and apply state-of-the-art machine learning and deep learning technologies.
Join the team in performing all the phases of a data science project: business understanding, data understanding and preparation, exploratory analysis, modeling, model evaluation, communicating results and business value of a model to the product leadership and finally deploying your data product.
Work with development teams & business groups to ensure models can be implemented as part of a delivered solution replicable across many clients.
Keep up to date with the latest developments in the field by continuous learning and proactively champion promising new methods relevant to the problems at hand.
Develop the ability to explain and present results to stakeholders.
Take the first step towards your dream career
Every Dell Technologies team member brings something unique to the table. Here’s what we are looking for with this role:
2nd or 3rd year university student, majoring in Data Science, Computer Science or similar discipline.
Familiarity with at least one scripting language such as Python and experience with Python libraries (such as numpy, pandas, etc…)
Strong applied mathematical and statistical skills regardless of the tools.
Demonstrated familiarity with ML and DL algorithms, diverse types of data and sources, different types of learning models, diverse learning settings.
Familiarity with visualization tools (Power BI, Tableau, Qlikview, etc.).
Demonstrated ability to propose novel solutions to problems, performing experiments to show feasibility of their solutions, and working to refine the solutions into a real-world context.
Strong analytical, written, and verbal communication skills.
Familiarity with ML libraries (such as SKLearn, MADlib, etc…) and with ML frameworks (such as TensorFlow, Keras, etc…)
Familiarity with using query languages such as SQL.
Ability to communicate well with senior-level team members