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

As a Crypto Data Scientist / Machine Learning Engineer at Token Metrics, you'll be responsible for optimizing machine learning systems, designing AI that automates predictive models, and enhancing our software's accuracy through careful statistical analysis and complex problem-solving with multi-layered data sets.

Candidates should have a Bachelor's degree in computer science, data science, mathematics, or a related field. Additionally, a Master’s degree is advantageous. A minimum of two years' experience in a related role and proficiency in Python, Java, and R is essential for success in the position.

Successful candidates should demonstrate advanced proficiency with ML frameworks, libraries, and data structures, combined with superb analytical and problem-solving skills. Familiarity with LLM fine-tuning and working with LLM Observability will be beneficial.

Token Metrics fosters a collaborative environment where team members can flourish by keeping abreast of the latest developments in machine learning and participating in open discussions, training sessions, and hands-on projects that promote professional growth.

At Token Metrics, we promote a positive and engaging work culture that emphasizes teamwork, creativity, and innovation. You'll find a supportive atmosphere that encourages collaboration and continuous learning, allowing you to thrive in your role as a Crypto Data Scientist / Machine Learning Engineer.

In interviewing for the Crypto Data Scientist / Machine Learning Engineer role, explain your structured approach—starting from understanding the problem, consulting with relevant stakeholders, selecting appropriate algorithms, and iteratively testing and refining your model to ensure it meets desired objectives.

Detail your experience with each programming language during the interview, highlighting specific projects or tasks completed in Python, Java, and R that demonstrate your proficiency and versatility as a Crypto Data Scientist / Machine Learning Engineer.

Discuss your methods for stress testing and performing statistical analyses to validate predictive models' accuracy, including any tools or frameworks you prefer to use, and how you would approach refining any models that fall short of expectations.

Demonstrate your commitment to professional development by mentioning resources like academic journals, online courses, conferences, or communities you engage with to stay updated in the rapidly evolving field of machine learning.

Be prepared to share a real-world scenario where you successfully tackled a multifaceted problem using machine learning. Outline the problem, your methodology, and the results you achieved, making sure to highlight your critical thinking and analytical skills.

Emphasize that collaboration is essential in machine learning projects. Share examples of how you've worked with cross-functional teams to gather requirements, share insights, and deliver high-value solutions, demonstrating effective communication skills.

Detail your strategies for handling large datasets, including tools you use for data storage and processing, as well as techniques for managing data analysis without compromising performance or accuracy in your machine learning models.

Mention your experience with popular machine learning libraries like TensorFlow, PyTorch, or scikit-learn, and explain why you favor certain tools based on project requirements, performance, or specific features that enhance your work as a Crypto Data Scientist / Machine Learning Engineer.

Discuss methodologies you apply to mitigate biases in algorithms, such as scrutinizing input data, employing techniques for fair prediction outcomes, and continuously validating model performance with diverse datasets to ensure equitable results.

Share examples of predictive models you've developed, discussing their objectives, the impact they had on decision-making or operational efficiency, and how they contributed to achieving business goals, particularly in a crypto context.