Machine Learning Engineer

As a Machine Learning Engineer at Cedar, your core responsibilities will include developing a profound understanding of Cedar's patients and product features, building and optimizing machine learning solutions, and collaborating closely with cross-functional teams to ensure effective implementation. You'll also evaluate the performance of your models and seek continuous improvement opportunities, making your role vital in enhancing Cedar's product offerings.

To qualify for the Machine Learning Engineer III position at Cedar, candidates should possess at least 5 years of experience with data, including a minimum of 3 years focusing on machine learning in an industry context. Additionally, expertise in SQL and Python is required, along with a solid understanding of machine learning techniques. Strong analytical abilities and excellent communication skills are also crucial to thrive in this role.

At Cedar, the Machine Learning Engineering team operates within the Product Engineering organization, collaborating closely with the Data Science, Product, and User Research teams. This collaborative environment fosters knowledge sharing and innovation, ensuring that machine learning solutions are effectively integrated into Cedar's healthcare products to maximize user experience and impact.

Machine Learning Engineers at Cedar can expect robust opportunities for growth as they enhance their ML capabilities and contribute to key product decisions. As the role evolves, engineers may expand their responsibilities and lead end-to-end projects, positioning themselves for career advancement in a rapidly scaling healthcare technology environment.

The Machine Learning Engineer role is central to Cedar's mission of improving healthcare affordability and accessibility. By developing tailored machine learning solutions, you will significantly enhance patient engagement and financial outcomes, thus driving a direct impact on the ongoing transformation of the healthcare experience for millions.

When building a machine learning model from scratch, I start with defining the problem clearly, followed by collecting and cleaning the relevant data. Then, I perform exploratory data analysis to understand the underlying patterns before selecting the appropriate algorithms. I ensure to validate my models using cross-validation techniques and continually refine them based on performance metrics.

I once worked on a model that predicted patient appointment no-shows. By analyzing feature importance and identifying irrelevant features, I optimized the model, which included adjusting hyperparameters and implementing ensemble methods. Ultimately, these changes improved our prediction accuracy by 15%, leading to better resource allocation.

Handling missing data varies by context, but I typically analyze the extent of missingness and how it might affect model performance. Depending on the situation, I might use imputation techniques, such as median filling or more sophisticated methods like K-Nearest Neighbors. Alternatively, if the missing data is a small percentage and does not significantly bias outcomes, I may choose to remove those entries.

I am proficient in various machine learning techniques, including regression, classification, clustering, and ensemble methods like random forests and gradient boosting. I also have experience in deep learning frameworks such as TensorFlow and PyTorch, enabling me to leverage advanced techniques for complex datasets.

To ensure scalability, I design machine learning solutions with modular components that can be independently scaled. I utilize cloud platforms like AWS for deployment and make use of efficient data pipeline practices to handle larger datasets. I also prioritize code optimization and use performance monitoring tools to gauge efficiency.

Feature engineering is crucial in my machine learning process as it directly influences model performance. I focus on creating meaningful features that enhance the model's predictive power. This can involve transforming existing data, combining datasets, or creating domain-specific features to give my models a more comprehensive understanding of the context.

In my previous position, I collaborated with product managers and designers to develop a healthcare predictive model. We held regular sync meetings to discuss project goals and user needs, ensuring our machine learning solutions aligned with business objectives while incorporating valuable feedback from diverse perspectives.

I measure the success of a machine learning model through various metrics depending on the problem. For classification tasks, accuracy, precision, and recall are key metrics. For regression tasks, I look at R-squared values and RMSE. Continuous performance monitoring post-deployment is also critical to assess real-world effectiveness.

To stay updated on machine learning trends, I follow leading journals, attend industry conferences, and participate in online forums. I also utilize platforms like Coursera and edX to take relevant courses and webinars that help refine my skills and keep me informed about the latest innovations.

I admire Cedar's mission to transform healthcare through data and technology. The opportunity to work on projects that directly impact patient experiences resonates with my passion for leveraging machine learning for social good. I also appreciate Cedar's innovative culture and commitment to collaboration, both of which align with my professional values.