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

As a Crypto Data Scientist / Machine Learning Engineer at Token Metrics, your primary responsibilities will include optimizing our machine learning systems, designing AI solutions, transforming data science prototypes into real-world applications, and ensuring the accuracy of our predictive models. You will collaborate closely with management to refine ML objectives and tackle complex data challenges, while also stressing the importance of document processes and staying updated with industry advancements.

To be considered for the Crypto Data Scientist / Machine Learning Engineer position at Token Metrics, candidates should hold a bachelor's degree in computer science, data science, mathematics, or a related field. A master’s degree is a plus, alongside at least two years of hands-on experience in machine learning engineering. Proficiency in programming languages such as Python, Java, and R, along with a solid understanding of ML frameworks and statistical methods, is essential.

Token Metrics harnesses the power of machine learning to build AI-driven crypto indices, rankings, and accurate price predictions. As a Crypto Data Scientist / Machine Learning Engineer, you’ll play a vital role in enhancing our algorithms which analyze vast amounts of historical data, offering insights that enable our customers to build more profitable crypto portfolios.

Candidates pursuing the Crypto Data Scientist / Machine Learning Engineer role at Token Metrics should possess superb analytical and problem-solving skills, alongside strong communication and collaboration abilities. Proficiency in advanced programming, extensive knowledge of ML libraries, and a deep understanding of statistics and algorithms are also critical for success in this position.

At Token Metrics, you'll find a dynamic and inclusive culture that fosters innovation and collaboration. As a Crypto Data Scientist / Machine Learning Engineer, you will be part of a diverse team of skilled professionals who are committed to pushing the envelope in the crypto analytics field. Continuous learning and staying at the forefront of technology developments are core to our values, making it an exciting environment to grow your career.

When answering this question, be specific about the Python libraries you have worked with, such as TensorFlow, Keras, or Scikit-learn. Discuss how you utilized these tools in past projects to build and optimize machine learning models, emphasizing the outcomes and any challenges you overcame.

Explain your methodology from understanding the problem statement to designing the system architecture. Highlight how you gather requirements, select appropriate ML algorithms, preprocess data, train models, and evaluate their performance. Incorporate examples to illustrate your thought process and experiences.

Discuss techniques such as cross-validation, hyperparameter tuning, and use of advanced metrics for evaluating model performance. Provide examples from past experiences where implementing these strategies led to improved accuracy and robustness of your models.

Share a specific project where you faced significant challenges, detailing the problem, your analysis, the algorithms you chose, and the insights gained. This allows you to showcase your analytical skills and your approach to problem-solving.

Mention key resources you utilize, such as research papers, online courses, and workshops, or communities you are part of within the machine learning and data science fields. Discuss how you apply this continuous learning to your work effectively.

Provide insights into your data preprocessing steps, including data cleaning, normalization, and feature selection. Highlight the importance of these processes in improving model performance and share examples of how you have successfully applied them.

Use this opportunity to showcase your analytical skills by detailing how you identified areas for improvement and the steps you took to optimize the model. Focus on the results of your optimization and any metrics that demonstrate success.

Talk about any relevant tools or frameworks you've utilized, like Apache Spark or Hadoop, and how they facilitated the handling of large datasets in your machine learning projects. This underscores your ability to work effectively with high volumes of data.

Explain the importance of documentation in your workflow. Share examples of how you create clear and detailed documentation for your algorithms, data sources, model training procedures, and findings to ensure repeatability and collaborative work.

Discuss how you ensure fairness, accountability, and transparency in your machine learning projects. Provide real examples of how you've navigated ethical dilemmas in data usage or model predictions, showcasing your commitment to responsible AI.