Data Science Engineer

As a Data Science Engineer at Pager Health, your responsibilities include developing predictive models to forecast healthcare trends, collaborating with stakeholders to identify business challenges, and utilizing machine learning and statistical methodologies to optimize healthcare outcomes. You'll work with cross-functional teams, design and deploy models, and evaluate product designs to improve our offerings.

To qualify for the Data Science Engineer position at Pager Health, candidates should have 5+ years of relevant experience, including 2+ years in data science or machine learning. A bachelor's degree in a quantitative field such as statistics or computer science is required. Experience with healthcare datasets, proficiency in programming languages like Python or R, and a background in machine learning are essential to succeed in this role.

Essential skills for a Data Science Engineer at Pager Health include a strong understanding of machine learning techniques, proficiency in data analysis tools such as SQL and Python, and the ability to work independently on complex projects. Familiarity with cloud platforms like GCP, biostatistics, and experience in deployable AI models will be beneficial to excel in this role.

At Pager Health, Data Science Engineers work in a remote-first, dynamic environment. The company promotes a collaborative culture, encouraging team members to engage with various departments across the globe. You will be part of a tight-knit team of creative and passionate professionals who are dedicated to transforming healthcare through technology.

At Pager Health, Data Science Engineers have numerous opportunities for growth, as the company encourages skill development and innovation. You’ll be able to shape your role, take on new challenges, and leverage the latest advancements in AI and machine learning, all while making a positive impact in the healthcare industry.

In discussing your experience with developing predictive models, focus on specific projects where you utilized machine learning techniques. Highlight your approach to data collection, model selection, evaluation metrics, and the impact your models had on outcomes or decision-making.

When answering this question, emphasize your communication skills and your willingness to understand the perspectives of different teams like engineering and product. Share examples of successful collaborations, how you’ve navigated challenges, and how you leverage input from stakeholders to enhance project outcomes.

Discuss your proficiency in programming languages such as Python or R, and mention any tools you’ve used for data analysis, model development, or visualization. Consider listing libraries or frameworks like TensorFlow, PyTorch, or scikit-learn that you're familiar with, and explain how they have contributed to your work.

Address how you implement rigorous testing and validation techniques when developing models. Discuss methods like cross-validation, performance metrics evaluation, and continuous monitoring to ensure your models remain accurate and applicable in real-time.

Share specific experiences where you’ve handled healthcare datasets, detailing the type of data you've worked with, the methods you used for analysis, and any insights you generated that impacted healthcare delivery. Highlight any regulatory knowledge that may apply to your work in healthcare analytics.

When discussing a time your model yielded unexpected results, focus on your analytical approach to identify the root cause. Explain how you adapted your model based on findings or external factors, as well as what you learned from that experience to improve future modeling.

In your response, mention sources like online courses, academic journals, industry conferences, or forums where you keep learning about the latest trends in data science and AI. Highlight your commitment to continuous education and how you’ve applied new knowledge to real-world projects.

Discuss the techniques you employ for feature selection, such as statistical tests, domain knowledge, feature importance rankings, or dimensionality reduction techniques. Emphasize the importance of selecting the right features to improve model performance.

Elaborate on the methods you employ to address missing data, such as imputation techniques, using algorithms that can handle missing values, or collecting additional data. Stress the importance of understanding how missing data may affect model assumptions and interpretations.

When discussing metrics for model evaluation, mention relevant metrics such as accuracy, precision, recall, F1-score, or area under the ROC curve, tailored to the specific context of the project. Describe how you leverage these metrics to assess and improve model effectiveness.