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Crypto Data Scientist / Machine Learning - LLM Engineer (Karachi-Remote)

Token Metrics is searching for a highly capable machine learning engineer to optimize our machine learning systems. You will be evaluating existing machine learning (ML) processes, performing statistical analysis to resolve data set problems, and enhancing the accuracy of our AI software's predictive automation capabilities.


As a machine learning engineer, you should demonstrate solid data science knowledge and experience.


A first-class machine learning engineer will be someone whose expertise translates into the enhanced performance of predictive models.


Responsibilities
  • Consulting with the manager to determine and refine machine learning objectives.
  • Designing machine learning systems and self-running artificial intelligence (AI) to automate predictive models.
  • Transforming data science prototypes and applying appropriate ML algorithms and tools.
  • Ensuring that algorithms generate accurate user recommendations.
  • Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks.
  • Developing ML algorithms to analyze huge volumes of historical data to make predictions.
  • Stress testing, performing statistical analysis, and interpreting test results for all market conditions.
  • Documenting machine learning processes.
  • Keeping abreast of developments in machine learning.


Requirements
  • Bachelor's degree in computer science, data science, mathematics, or a related field.
  • Master’s degree in computational linguistics, data science, data analytics, or similar will be advantageous.
  • At least two years' experience as a machine learning engineer.
  • Advanced proficiency with Python, Java, and R code.
  • Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.
  • LLM fine-tuning experience and working with LLM Observability
  • In-depth knowledge of mathematics, statistics, and algorithms.
  • Superb analytical and problem-solving abilities.
  • Great communication and collaboration skills.
  • Excellent time management and organizational abilities.


About Token Metrics


Token Metrics helps crypto investors build profitable portfolios using artificial intelligence based crypto indices, rankings, and price predictions. 


Token Metrics has a diverse set of customers, from retail investors and traders to crypto fund managers, in more than 50 countries.

What You Should Know About Crypto Data Scientist / Machine Learning - LLM Engineer (Karachi-Remote), Token Metrics

Token Metrics is on the lookout for a talented Crypto Data Scientist/Machine Learning Engineer to join our dynamic team in Karachi—offering a remote opportunity! If you’re passionate about leveraging machine learning to enhance AI capabilities, this is your chance to shine. You will dive deep into existing ML processes, conducting statistical analyses to tackle data set issues while boosting the accuracy of our predictive models. You'll work closely with the management to refine our machine learning objectives, design self-running AI systems, and ensure that our algorithms deliver top-notch user recommendations. With your strong data science knowledge and experience, you will take on the exciting challenge of solving complex problems using vast multi-layered data sets. Not to mention, you’ll be developing ML algorithms that analyze historical data, stress testing them to interpret results under various market conditions. Staying updated with the latest developments in machine learning is crucial, and your contributions will directly impact our clients' investment success. If you’re ready to use your skills with languages like Python, Java, and R and have expertise in ML frameworks, then this is the ideal position for you. Join Token Metrics and be part of shaping the future of crypto investments!

Frequently Asked Questions (FAQs) for Crypto Data Scientist / Machine Learning - LLM Engineer (Karachi-Remote) Role at Token Metrics
What are the key responsibilities of a Crypto Data Scientist at Token Metrics?

As a Crypto Data Scientist at Token Metrics, you will be responsible for optimizing machine learning systems, designing AI algorithms to automate predictive models, and ensuring the accuracy of user recommendations. You will also tackle complex data sets, perform stress tests, and document machine learning processes to enhance overall performance.

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What qualifications do I need to become a Crypto Data Scientist at Token Metrics?

To become a Crypto Data Scientist at Token Metrics, you should hold a Bachelor's degree in computer science, data science, mathematics, or a related field. A Master’s degree is advantageous. Additionally, you should have at least two years of experience as a machine learning engineer, demonstrating proficiency in Python, Java, and R.

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Is experience required for the Machine Learning Engineer role at Token Metrics?

Yes, Token Metrics requires at least two years of experience as a machine learning engineer. Your background should reflect advanced proficiency in relevant programming languages and in-depth knowledge of machine learning frameworks and algorithms.

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What skills are necessary for a Machine Learning Engineer position at Token Metrics?

For a Machine Learning Engineer position at Token Metrics, essential skills include advanced proficiency in Python, Java, and R, along with expertise in ML frameworks and libraries. Strong analytical and problem-solving skills, superb communication, and collaboration abilities are crucial for success in this role.

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How does Token Metrics support continuous learning for Machine Learning Engineers?

Token Metrics fosters an environment of continuous learning by encouraging our Crypto Data Scientists to stay informed about the latest developments in machine learning. This includes workshops, training sessions, and access to additional resources to enhance their skills and knowledge.

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Common Interview Questions for Crypto Data Scientist / Machine Learning - LLM Engineer (Karachi-Remote)
Can you explain your experience with machine learning frameworks?

In responding to this question, highlight the specific machine learning frameworks you've used, such as TensorFlow or PyTorch. Discuss any projects where you implemented these frameworks to solve real-world problems, focusing on your contributions and the outcomes achieved.

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How do you handle multi-layered data sets?

When addressing this question, describe your approach to managing complexity within data sets. Explain the techniques you use to preprocess data, manage challenges, and discuss instances where this skill helped you derive insights or improve model performance.

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What strategies do you apply to optimize machine learning algorithms?

Discuss the methodologies you deploy, such as hyperparameter tuning, feature selection or engineering, and your experience applying these to enhance algorithm performance. Providing specific examples will help illustrate your effectiveness in this area.

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Can you provide an example of how you improved a predictive model's accuracy?

To answer this, cite a specific project where you successfully improved a model. Detail the initial challenges, the steps you took to enhance its accuracy, and the measurable results that followed, showcasing your problem-solving capabilities.

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What experience do you have with LLM fine-tuning?

Discuss your hands-on experience with fine-tuning large language models. Share any relevant projects where you adapted models for specific tasks, highlighting the techniques used and the improvements in performance or user experience.

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How do you ensure your machine learning models are scalable?

Explain your process for building scalable models and mention tools and practices you employ, like using efficient data structures and managing workload distribution, possibly referencing previous experiences where you ensured model scalability.

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What role does statistical analysis play in your machine learning work?

Describe how you utilize statistical analysis to inform your decisions within machine learning. Talk about specific techniques you use to assess model performance and data set quality, and provide examples of how this has impacted your projects.

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Can you explain your approach to documentation in machine learning projects?

Share the importance you place on documentation for machine learning processes. Detail your methods for creating clear, thorough documentation that aids collaboration and contributes to the maintainability of your models.

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How do you stay updated with advancements in machine learning?

Discuss your strategies for continuous learning, such as attending conferences, participating in online courses, or reading journals in machine learning. Highlight specific resources you follow that contribute to your knowledge.

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What challenges have you faced in your machine learning projects, and how did you overcome them?

Provide examples of significant challenges, such as data quality issues or model convergence problems. Talk about the steps you took to overcome these obstacles, demonstrating your critical thinking and resilience in finding solutions.

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Token Metrics is a cryptocurrency investment research platform that’s driven by machine learning and artificial intelligence. The company was founded by Ian Balina in 2017 as he traded his way from $2...0,000 to more than $5 million, logging all h...

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Full-time, remote
DATE POSTED
January 10, 2025

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