Machine Learning Intern

Your Journey at Crowe Starts Here: At Crowe, you can build a meaningful and rewarding career. With real flexibility to balance work with life moments, you’re trusted to deliver results and make an impact. We embrace you for who you are, care for your well-being, and nurture your career. Everyone has equitable access to opportunities for career growth and leadership. Over our 80-year history, delivering excellent service through innovation has been a core part of our DNA across our audit, tax, and consulting groups. That’s why we continuously invest in innovative ideas, such as AI-enabled insights and technology-powered solutions, to enhance our services. Join us at Crowe and embark on a career where you can help shape the future of our industry.

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately overlapping the academic summer. DS interns will have a designated data scientist mentor and will spend the majority of their time working on a data science project overseen by that mentor. The expectation is that at the end of the internship, functional and demonstrable progress has been made in a way that can be integrated into the functional portfolio of the team. Important: the data science internship does not necessarily transition to a full-time role at the end of the period. Depending on team need, firm budget, and fit, interns should not expect their time to conclude with an offer. Here are some example projects that may be prioritized for an internship: Anomaly detection using deep neural networks Numerical optimization applied to problems in manufacturing Personal identifiable information (PII) and personal health information (PHI) detection in natural language Areas of Focus The data science internship is intended to expose the intern to what it means to do data science on a team in an enterprise setting. To that end, there are three major components with which an intern should expect to engage. Modeling Understanding how to frame business problems as data science problems Navigating the full data science lifecycle: research and exploration, development, deployment, support Using correct model selection and training procedures to prevent common modeling errors Creating reasonable test scenarios and diagnostic measures Software Development Writing high quality Python (readable, reusable, modular, and well-abstracted code) Using Linux, the terminal, and other core development tools Understanding source control (git), containerization (docker) and deployment technologies (cicd/k8s) Participating in code reviews Business Value and Processes Contributing to team development processes and agile/scrum rituals Helping to clarify and prioritize work, define success criteria, and own work item completion Applying all relevant change management controls for risk and compliance policies Presenting work clearly to technical and non-technical audiences Relevant majors and areas of expertise Data Science Computer Science Mathematics Natural or Social Sciences Relevant additional backgrounds also considered Essential Job Functions: • Anomaly detection using deep neural networks • Numerical optimization applied to problems in manufacturing • Personal identifiable information (PII) and personal health information (PHI) detection in natural language • Understanding how to frame business problems as data science problems • Navigating the full data science lifecycle: research and exploration, development, deployment, support • Using correct model selection and training procedures to prevent common modeling errors • Creating reasonable test scenarios and diagnostic measures • Writing high quality Python (readable, reusable, modular, and well-abstracted code) • Using Linux, the terminal, and other core development tools • Understanding source control (git), containerization (docker) and deployment technologies (cicd/k8s) • Participating in code reviews •