Machine Learning Developer (WFH) TWN

As a Machine Learning Developer at ISTA Solutions, you'll be tasked with developing and implementing machine learning models to solve intricate business challenges. This includes everything from performing detailed data analysis and feature engineering to validating datasets and ensuring the models are scalable and maintainable. Collaborative integration of models into existing systems and creating user-friendly dashboards for presenting data insights are also key parts of your role.

To qualify for the Machine Learning Developer position at ISTA Solutions, you should have 1 to 2 years of relevant experience, a background in engineering or data science, and strong Python skills. Familiarity with machine learning algorithms like Random Forest and Gradient Boosting, as well as proficiency in using libraries such as Dash or Streamlit for dashboard creation, is highly beneficial.

While not mandatory, having healthcare experience can be advantageous for the Machine Learning Developer role at ISTA Solutions. Since you'll be applying machine learning techniques to healthcare-specific problems, familiarity with the industry can give you a better edge in understanding the nuances and challenges involved.

For the Machine Learning Developer role at ISTA Solutions, strong proficiency in Python is essential, particularly for data analysis and building dashboards. You'll also be expected to be familiar with various machine learning frameworks and libraries, including those used for visualization, such as Dash, Streamlit, Panel, and Bokeh.

ISTA Solutions is committed to the continuous learning and career development of its Machine Learning Developers. We create opportunities for professional growth through a supportive work environment and access to resources that encourage skill enhancement and innovation, ensuring you thrive and advance in your career.

When answering this question, illustrate your involvement in a specific project by detailing your role, the problem you aimed to solve, the algorithms you implemented, and the impact of your solutions. Focus on demonstrating how your approach contributed to the overall success of the project.

Discuss your experience with various algorithms such as Random Forest, Gradient Boosting, or AutoML. Provide context about when and why you chose these algorithms in past projects, highlighting their features and the outcomes you achieved.

Explain techniques you've used to manage overfitting, such as cross-validation, pruning, tuning hyperparameters, or using regularization methods. Providing examples from past work can showcase your practical understanding of the issue.

Detail your systematic approach to data cleaning and preprocessing, which may include handling missing values, removing duplicates, normalization, and feature scaling. Highlight how these steps impact the performance of your models.

Discuss your approach to feature engineering and how it substantially affects model accuracy. Include examples of features you've engineered in past projects and how they enhanced model performance.

Share your strategies for creating maintainable models, such as documentation, modular code, and version control. This shows your understanding of the long-term necessity of maintaining ML models in a production environment.

Talk about the libraries you've worked with, like Dash or Streamlit, and provide examples of dashboards you've developed. Highlight how your dashboards communicated insights effectively to stakeholders.

Explain how you assess model performance using metrics such as accuracy, precision, recall, or F1 score. Discuss any experiences using different methodologies based on the nature of the project.

Share the resources you regularly explore, such as academic journals, conferences, online courses, or community forums. Discuss how implementing new knowledge has positively impacted your work.

Provide a specific example of a scenario where you had to distill complicated insights into simpler terms. Focus on your approach to making the information accessible while ensuring the audience understood the implications of your findings.