Data Science and Engineering Intern

Company Description

At Intuitive, we are united behind our mission: we believe that minimally invasive care is life-enhancing care. Through ingenuity and intelligent technology, we expand the potential of physicians to heal without constraints.

As a pioneer and market leader in robotic-assisted surgery, we strive to foster an inclusive and diverse team, committed to making a difference. For more than 25 years, we have worked with hospitals and care teams around the world to help solve some of healthcare's hardest challenges and advance what is possible.

Intuitive has been built by the efforts of great people from diverse backgrounds. We believe great ideas can come from anywhere. We strive to foster an inclusive culture built around diversity of thought and mutual respect. We lead with inclusion and empower our team members to do their best work as their most authentic selves.

Passionate people who want to make a difference drive our culture. Our team members are grounded in integrity, have a strong capacity to learn, the energy to get things done, and bring diverse, real world experiences to help us think in new ways. We actively invest in our team members to support their long-term growth so they can continue to advance our mission and achieve their highest potential.

Join a team committed to taking big leaps forward for a global community of healthcare professionals and their patients. Together, let's advance the world of minimally invasive care.

Job Description

Primary Function of Position:

You will be a part of a small team working on high impact tasks with multiple stakeholders.  As a Data Science and Engineering Intern you will support our data science and engineering teams in the development, validation, and implementation of data-driven solutions. Leveraging a foundation in both statistical methods and computer science, you will assist in analyzing complex datasets to extract insights, build predictive and prescriptive models, and collaborate with cross-functional teams to integrate these solutions into our broader technology and product initiatives. In addition to hands-on technical tasks, you will contribute to the end-to-end data project lifecycle, from problem definition and data acquisition to result communication and documentation.

Roles & Responsibilities:

In addition to your primary responsibilities, you will be engaged in a variety of tasks integral to our team's function. You will develop and validate data-driven models, delve into datasets to identify patterns and trends, and ensure data is meticulously cleaned and prepared for analysis. Prototyping solutions and refining them through iterative feedback is also key. Collaborative efforts with data scientists, engineers, and product teams will be essential to ensure that solutions are in line with organizational goals. Effective documentation and communication of methodologies and findings are vital, as is presenting these insights to stakeholders. Commitment to continuous learning is expected, keeping abreast of the latest developments in data science and participating in educational opportunities. You'll contribute to software development, embedding data models into production systems with well-structured code. Monitoring the performance of deployed models for accuracy and recalibrating as necessary is crucial, as is upholding ethical standards in data handling and modeling. Finally, your role encompasses assisting with project management, helping to define and track progress against project goals and timelines.

Qualifications

University Hiring Program Eligibility Requirements:

• University Enrollment: Must be currently enrolled in and returning to an accredited degree-seeking academic program in the Fall.
• Internship Work Period: Must be available to work full-time (approximately 40 hours per week) during a 10–12-week period starting May or June. Specific start dates are shared during the recruiting process.

Skills:

In your multifaceted role, you will leverage robust statistical analysis skills to interpret complex datasets and demonstrate proficiency in programming languages such as Python, R, or Java. Your familiarity with machine learning will be enhanced by a deep understanding of cybersecurity algorithms and the application of AI to strengthen data protection frameworks. Experience with popular machine learning libraries like PyTorch, TensorFlow or scikit-learn is expected, as is expertise in data wrangling, ensuring data cleanliness and transformation. You must also articulate complex data insights in an accessible manner. Additionally, a foundational knowledge of software development, including best practices and versioning tools like Git, is essential, coupled with competence in SQL and NoSQL database systems. Your problem-solving abilities will be critical for navigating unexpected chall