Data Scientist I - AI Platform

As a Data Scientist I at GHX, your main responsibilities include collaborating with stakeholders to identify data-driven solutions, developing automated data pipelines, conducting data analysis, and creating visualizations that communicate findings to stakeholders effectively. You'll also work on model development and validation to support our AI initiatives, helping drive better insights and decision-making across the organization.

To qualify for the Data Scientist I role at GHX, you should have a BS in computer science, applied mathematics, or a related field. In addition to strong programming skills in languages like Python, R, and SQL, you should have experience using AWS technologies and version control systems like Git. A passion for learning and mastering new technologies will also be invaluable.

A Data Scientist I at GHX should be proficient in various programming languages, particularly Python, R, SQL, and Matlab. Familiarity with C/C++ and experience interacting with APIs will also be beneficial in developing complex data analysis and algorithms that address the needs of diverse stakeholders.

GHX is committed to fostering a culture of learning and development. As a Data Scientist I, you will have access to various resources and opportunities for continuous education, workshops, and mentorship programs to enhance your skills in data science, MLOps, and related technologies, helping you grow both personally and professionally.

At GHX, you can expect a highly collaborative environment where you'll work closely with cross-functional teams. Data scientists engage with stakeholders from different departments, sharing insights and brainstorming solutions. The inclusive workplace culture emphasizes valuing diverse perspectives, which fosters creativity and innovation in solving healthcare challenges.

When answering this question, share specific examples of machine learning projects you've worked on, the algorithms you used, and the outcomes. Highlight your approach to selecting the right model for the data and the importance of validating results to ensure reliability.

Show your problem-solving skills by discussing various techniques such as imputation, data cleaning, or using algorithms robust to missing data. Explain how your approach aligns with maintaining data integrity while still delivering results.

This question tests your communication skills. Choose a topic, like regression analysis, and break it down into layman's terms. Emphasize the relevance and implications of the concept, showing that you can both analyze data and share insights effectively.

Discuss your familiarity with AWS services relevant to data science, such as S3 for data storage and SageMaker for model training. Provide examples of projects where you’ve deployed models or managed data pipelines on AWS.

Explain how data visualization is crucial for interpreting results and communicating insights effectively. Provide examples of tools you’ve used and how visual representations impacted decision-making in previous roles.

Choose a significant project that had obstacles, detailing how you identified issues and crafted solutions. Discuss your analytical methods, teamwork, and what you learned in the process, showcasing your resilience and problem-solving skills.

Mention the statistical methods you're comfortable with, such as hypothesis testing or regression analysis, and explain their significance in extracting meaningful insights from data. Relate this to real-world applications in decision-making.

Share your strategies for continuous learning, such as participating in online courses, attending workshops, joining professional organizations, or following relevant publications and influencers in the data science community.

Discuss practices like code reviews, unit testing, and documentation to maintain high-quality standards in your work. Demonstrate your understanding of the importance of making your work reproducible and maintainable.

Explain your approach to prioritization, possibly utilizing factors like project impact, resource availability, and deadlines. Discuss examples where you've successfully juggled multiple tasks and how you ensured timely delivery without sacrificing quality.