Crypto Data Scientist / Machine Learning Engineer (Malaysia-Remote)

As a Crypto Data Scientist / Machine Learning Engineer at Token Metrics, your primary role revolves around optimizing our machine learning systems to enhance predictive analytics in the crypto space. You will be responsible for evaluating current ML practices, designing AI-driven models, and solving complex data challenges to provide accurate recommendations for our users.

To qualify for the Crypto Data Scientist / Machine Learning Engineer position at Token Metrics, you should hold a bachelor’s degree in computer science, data science, mathematics, or a related field. A master's degree in a relevant field is advantageous, alongside at least two years of direct experience in machine learning and advanced programming skills in Python, Java, or R.

Applicants for the Crypto Data Scientist / Machine Learning Engineer role at Token Metrics should have at least two years of relevant experience, showcasing a strong background in machine learning, data modeling, and statistical analysis. Experience working with diverse data sets and knowledge of ML frameworks are crucial to success in this position.

For the Crypto Data Scientist / Machine Learning Engineer position at Token Metrics, advanced proficiency in programming languages such as Python, Java, and R is essential. These languages are critical for developing algorithms, analyzing data, and ensuring efficient execution of machine learning processes.

Key responsibilities of a Crypto Data Scientist / Machine Learning Engineer at Token Metrics include designing machine learning systems, developing predictive algorithms, performing statistical analysis, documenting processes, and collaborating with management to refine ML objectives. Your work will directly impact the effectiveness of our AI solutions.

Token Metrics is committed to supporting the professional development of its Crypto Data Scientists / Machine Learning Engineers by providing opportunities for continued learning, access to cutting-edge tools, and an environment that encourages innovation. Our team is passionate about keeping up with the latest trends and technologies in machine learning.

Yes! The position of Crypto Data Scientist / Machine Learning Engineer at Token Metrics is designated as remote, allowing you to work from Malaysia while still being an integral part of our dynamic team. This flexibility is designed to help you maintain a work-life balance while contributing to our mission.

When designing a machine learning system, key elements to consider include defining the problem clearly, selecting appropriate algorithms, gathering and preparing high-quality data, and considering factors such as scalability, efficiency, and performance metrics. Discussing past experiences can illustrate your understanding effectively.

Evaluating the performance of a machine learning model can involve several metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. It is crucial to use cross-validation and ensure that the chosen metric aligns with the business goals, providing insights into the model’s effectiveness.

When discussing a machine learning project, focus on the objectives you aimed to achieve, the methods and algorithms you used, the data you worked with, and the results or impact of your work. Highlighting specific challenges and how you addressed them can illustrate your problem-solving skills.

Effective feature selection strategies include using techniques such as LASSO, tree-based models, and recursive feature elimination, as well as employing domain knowledge to identify relevant features. Discuss how you decide which features to keep based on model performance and business context.

Handling overfitting can involve techniques like cross-validation, simplifying models, and using regularization methods. You can discuss practical cases where you've implemented these solutions, demonstrating a clear understanding of model generalization.

Data cleaning is fundamental in machine learning as it directly influences the quality of the input data, which in turn affects model accuracy. Explain your experience with data preprocessing, identifying missing values, and ensuring data consistency to provide context around the importance of this step.

Staying current in machine learning involves continuous learning through online courses, reading research papers, attending conferences, and engaging with the community. Share specific examples of resources or networking events that have contributed to your growth in this dynamic field.

The bias-variance trade-off is crucial for understanding model performance. A high bias model may underfit the data, while a high variance model may overfit it. Discussing how you balance these factors in model selection and training can showcase your depth of knowledge.

Automating machine learning processes can involve using tools like AutoML, pipelines for data processing, and libraries that facilitate repetitive tasks. Discuss any specific frameworks or tools you have employed, highlighting the efficiency gained in your previous projects.

Dealing with missing or corrupted data can involve extracting, imputing, or removing affected records, depending on the context. Discuss your experience applying various imputation strategies and how you ensure the dataset remains representative of the original conditions.