Crypto Data Scientist / Machine Learning - LLM Engineer Intern
Houston, USA
Full time
Remote
Compensation is not specified
Role
Data Scientist
Description
Responsibilities
- Consulting with management to identify and refine machine learning goals.
- Planning machine learning systems and autonomous AI for predictive model automation.
- Converting data science prototypes and implementing relevant ML algorithms and technologies.
- Ensuring the accuracy of user recommendations generated by algorithms.
- Addressing intricate problems with complex data sets and optimizing existing ML frameworks.
- Creating ML algorithms to analyze large historical data volumes for predictions.
- Conducting stress testing, statistical analysis, and result interpretation across various market conditions.
- Documenting machine learning processes.
- Staying updated on advancements in machine learning.
Requirements
- Bachelor's degree in computer science, data science, mathematics, or related field.
- A Master’s degree in computational linguistics, data science, data analytics, or similar is a plus.
- At least two years of experience as a machine learning engineer.
- Proficiency in Python, Java, and R coding.
- Extensive familiarity with ML frameworks, libraries, data structures, data modeling, and software architecture.
- Experience in LLM fine-tuning and LLM Observability.
- In-depth understanding of mathematics, statistics, and algorithms.
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration abilities.
- Effective time management and organizational skills.
- Experience with crypto or web3 projects.
About Token Metrics
Token Metrics assists cryptocurrency investors in constructing lucrative portfolios utilizing AI-powered crypto indices, rankings, and price forecasts. With a broad customer base encompassing retail investors, traders, and crypto fund managers in over 50 countries.
Skills Required

Token Metrics
Website
www.tokenmetrics.comCompany size
Not specified
Location
United States
Description
Not specified