Senior Artificial Intelligence Engineer 2

As a Senior Artificial Intelligence Engineer 2 at ModMed, you will be tasked with developing and implementing machine learning models, analyzing large healthcare datasets, and optimizing healthcare delivery through predictive analytics. Your keen insights will help uncover trends and enhance existing products, making a meaningful impact in the healthcare sector.

To qualify for the Senior Artificial Intelligence Engineer 2 role at ModMed, candidates should possess a Bachelor’s or Master’s degree in Data Science, Computer Science, or a related field, along with 6+ years of relevant experience in machine learning and data analytics, particularly within healthcare or data-intensive environments.

Essential technical skills for the Senior Artificial Intelligence Engineer 2 at ModMed include proficiency in programming languages such as Python and R, experience with machine learning frameworks like TensorFlow or PyTorch, and familiarity with big data platforms such as Hadoop or Spark. Strong data manipulation skills using libraries like Pandas and NumPy are also crucial.

ModMed is committed to the professional growth of its team members. For a Senior Artificial Intelligence Engineer 2, this includes opportunities for continuous learning through tuition reimbursement programs, access to platforms like LinkedIn Learning, and participation in Business Resource & Special Interest Groups to foster engagement and community-building.

At ModMed, work-life balance is a priority. Senior Artificial Intelligence Engineers 2 can enjoy generous paid time off, flexible scheduling, and wellness allowances. The company also provides family-friendly leave policies, ensuring employees can maintain a healthy balance between their professional and personal lives.

When responding to this question, highlight specific projects where you utilized machine learning to solve healthcare-related problems. Discuss the types of models you developed and how your work contributed to improving patient outcomes.

Use the STAR method (Situation, Task, Action, Result) to structure your response. Explain the challenge, your approach to analyzing the data, and the successful outcomes you achieved.

Share your strategies for staying updated, such as following influential blogs, participating in online courses, attending conferences, or engaging in AI communities. Show your passion for continuous learning and innovation.

Discuss metrics such as accuracy, precision, recall, and F1 score to evaluate the performance of your models. Explain how each metric applies to ensuring that your predictions are valuable in a healthcare context.

Illustrate your systematic approach to resolving data inconsistencies. Describe how you would identify the source of the issue, tools you would employ to correct data quality, and the importance of maintaining integrity in healthcare analytics.

Detail your experience with healthcare data formats like HL7 and FHIR, and your understanding of HIPAA regulations. Discuss how you ensure compliance while working with sensitive patient data.

Offer an example that demonstrates your communication skills. Explain how you tailored your message to an audience unfamiliar with the technical jargon while effectively conveying the essential ideas.

Discuss any hands-on experience you have with big data technologies like Hadoop or Spark. Highlight how you have utilized these tools in past projects to analyze large datasets effectively.

Explain your strategy for prioritizing tasks, such as using productivity tools, setting deadlines, and assessing the urgency and importance of each project. Share examples of how you've successfully managed competing demands.

Share your preferred algorithms based on the projects you've worked on, and explain why they are effective in healthcare analytics. Discuss scenarios in which particular algorithms excel and how they have driven successful outcomes.