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

As a Crypto Data Scientist / Machine Learning Engineer at Token Metrics, your responsibilities include consulting with management to define machine learning objectives, designing self-running AI systems to automate predictive models, and applying various ML algorithms to enhance performance. Additionally, you'll be working on transforming data science prototypes, optimizing existing machine learning libraries, and ensuring accuracy in user recommendations.

To qualify for the role of Crypto Data Scientist / Machine Learning Engineer at Token Metrics, you need a Bachelor's degree in computer science, data science, mathematics, or a related field. A Master’s degree in a relevant area is advantageous, along with at least two years of experience in machine learning engineering. Proficiency in programming languages like Python, Java, and R is essential, alongside extensive knowledge of ML frameworks and data structures.

Key skills essential for success as a Crypto Data Scientist / Machine Learning Engineer at Token Metrics include advanced programming abilities in Python, Java, and R, strong analytical and problem-solving capabilities, and in-depth knowledge of mathematics, statistics, and algorithms. Familiarity with LLM fine-tuning is also important. Communication and time management skills will help you thrive in this collaborative environment.

Token Metrics is committed to the professional growth of its Crypto Data Scientists / Machine Learning Engineers by fostering a culture of continuous learning. We encourage our team members to keep up-to-date with the latest developments in machine learning and provide resources for skill enhancement and training opportunities. Our collaborative environment also allows for knowledge sharing among colleagues.

At Token Metrics, the work culture for a Crypto Data Scientist / Machine Learning Engineer is dynamic and innovative. Our remote-first approach promotes flexibility and encourages a healthy work-life balance. Team collaboration is key, and we value open communication and the sharing of ideas. You'll be joining a diverse and talented group of professionals passionate about leveraging technology to assist crypto investors.

When discussing your experience, highlight specific ML algorithms you've implemented in past projects. Talk about how you've selected those algorithms based on the nature of the data and the objectives of the project. Moreover, you can share results and improvements observed through the use of those algorithms.

Describe your systematic approach to optimizing machine learning models, starting from data preprocessing to feature selection, model selection, and fine-tuning hyperparameters. It’s also beneficial to mention the tools and techniques you utilize to evaluate model performance.

Discuss any experience you have working with big data technologies such as Hadoop, Spark, or similar frameworks. Mention specific projects where you've handled large datasets, the challenges faced, and how you successfully managed them to achieve your goals.

To ensure accuracy, talk about your techniques, like cross-validation, and validation sets, and the importance of maintaining a proper data split. Share examples where you’ve iterated on model training to achieve higher accuracy and how you monitor performance over time.

If you have experience with LLM fine-tuning, provide details about projects where you've enhanced model performance through this process. Explain your approach on how to select and prepare datasets for fine-tuning, and the impact it had on outcomes.

Share your strategy for keeping up with industry trends—whether it's through conferences, webinars, academic journals, or online courses. Mention specific resources or communities you are a part of, emphasizing your commitment to continuous learning.

When answering this, detail the project scope, the challenges faced (e.g., data quality, integration), and the results achieved. This gives insights into your problem-solving capabilities and resilience under pressure.

Discuss examples of past collaboration experiences. Highlight how you coordinate with data engineers, product managers, and stakeholders, illustrating your teamwork skills and ability to integrate feedback into your work.

Mention the tools you are familiar with such as R, Python's libraries like pandas and NumPy, or specialized software. Elaborate on why you prefer these tools based on their capabilities, user experience, and how they fit your workflow.

When asked this, express genuine interest in Token Metrics' mission. Discuss how your skills and passion align with the company's goals, and share what excites you about contributing to its innovative projects in the crypto space.