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

As a Crypto Data Scientist / Machine Learning Engineer at Token Metrics, you'll take on a variety of responsibilities that include consulting with management to determine machine learning objectives, designing systems for self-running AI, transforming data prototypes with the appropriate ML algorithms, and ensuring the accuracy of algorithms to provide quality user recommendations. Additionally, you'll solve complex problems related to data sets and optimize existing ML libraries to enhance overall performance.

To qualify for the Crypto Data Scientist / Machine Learning Engineer role at Token Metrics, candidates should possess a bachelor's degree in computer science, data science, mathematics, or related fields, with a master's degree being an added advantage. Experience-wise, at least two years as a machine learning engineer is essential. Candidates should also have advanced proficiency in programming languages such as Python, Java, and R, along with a solid grasp of ML frameworks and analytical skills.

Token Metrics leverages machine learning to empower investors with advanced AI-driven indices, rankings, and price predictions that aid in constructing profitable crypto portfolios. By employing sophisticated algorithms, we can analyze historical data and market trends, allowing our customers to make informed decisions about their investments. As a Crypto Data Scientist / Machine Learning Engineer, you'll play a crucial role in refining these systems.

Success in the Crypto Data Scientist / Machine Learning Engineer role at Token Metrics hinges on several key skills, including advanced proficiency in programming (especially in Python, Java, and R), a strong knowledge of ML frameworks, sharp analytical and problem-solving abilities, and excellent collaboration skills. Additionally, familiarity with LLM fine-tuning and observability will give candidates an edge.

As a Crypto Data Scientist / Machine Learning Engineer at Token Metrics, staying updated with advancements such as new machine learning algorithms, emerging tools, and trends in AI technology is vital. The field is rapidly evolving, and being aware of state-of-the-art techniques can significantly enhance your contributions to our systems and the accuracy of our predictive models.

Certainly! When answering this question, you can discuss specific algorithms you’ve used in your previous roles, how you applied them to solve real-world problems, and any challenges you overcame. Providing examples of projects where you've implemented these algorithms will illustrate your expertise and practical knowledge.

To optimize a machine learning model, I usually begin by assessing the quality of the data, ensuring it’s clean and properly structured. Next, I tune hyperparameters, select relevant features, and evaluate the model using cross-validation techniques. It’s also essential to analyze the output to understand where the model can be improved further.

When dealing with overfitting, I typically employ techniques such as cross-validation, regularization, or pruning in decision trees. Additionally, I might opt for simpler models with fewer parameters or gather more data to train on, which can help improve generalization.

In discussing a challenging dataset, focus on a specific example where you encountered issues, like missing data or class imbalance. Highlight the methods you used to clean the dataset, balanced the classes, or applied advanced techniques to extract valuable insights from it, demonstrating your problem-solving abilities.

I am most comfortable with Python, Java, and R. For instance, I extensively used Python libraries like Pandas and Scikit-learn for data manipulation and machine learning tasks in past projects. Explaining real-world applications of these languages will show your proficiency and suitability for the role.

To assess a model's performance, I usually leverage metrics such as accuracy, precision, recall, and F1 score, depending on the problem type. By splitting the data into training and testing sets, I can evaluate the model’s performance in unseen situations, which gives insight into its reliability and effectiveness.

Documentation plays a crucial role in machine learning projects as it provides clarity on processes, model choices, and parameter settings. It also aids in collaboration within the team and ensures that future work on the project can seamlessly build on previous efforts. Good documentation is a key aspect of sustainable project management.

When discussing collaboration, reference specific projects where you worked closely with teammates, data engineers, or stakeholders to achieve a common goal. Share how you managed communications, divided responsibilities, and integrated feedback from team members to enhance the project's outcome.

I stay current with machine learning trends by actively reading research papers, following influential blogs, and participating in webinars and online courses. Engaging in communities on platforms like LinkedIn and GitHub also helps me to learn from peers and bring fresh insights to my work.

In discussing your experience with LLM fine-tuning, be sure to explain the specific projects where you have applied this technique, what the original models were, and the outcomes after fine-tuning. This showcases your hands-on expertise and understanding of cutting-edge machine learning techniques.