Remote Python Data Analyst Jobs

About Turing.com:

Turing?s mission is to unleash the world?s untapped human potential. We use AI to source, evaluate, hire, onboard, and manage engineers remotely (including the HR and compliance aspects) in a bigger platform that we call the ?Talent Cloud?.

We recently achieved unicorn status with a valuation of $1.1B, after raising over $140M in financing over four rounds of funding. 1000+ companies including companies like Johnson & Johnson, Pepsi, Dell, Disney +, have hired Turing developers.

About the role:

We're seeking Python developers passionate about web development and proficient in LLMs to create highly scalable, customer-centric, and efficient web applications.

Job responsibilities:

• Develop Python scripts tailored for data analysis and visualization.
• Ensure the accuracy and functionality of Python code through rigorous testing and debugging, particularly in LLM applications.
• Create and validate algorithms using Python.
• Utilize Python libraries to visualize content and generate insightful reports.
• Stay updated on Python, LLM, and Gen AI advancements through ongoing training and research.
• Deploy and maintain Python solutions for LLM applications, promptly addressing issues.
• Keep abreast of advancements in generative AI, such as LLMs, GPTs, GANs, VAEs, and related techniques.
• Evaluate large datasets for quality, accuracy, and perform advanced data analysis.

Job requirements:

• Bachelor's/Master's degree in computer science or equivalent experience.
• 3+ years of professional experience in software development, focusing on Python.
• Demonstrated expertise in designing and implementing generative models with deep learning frameworks like TensorFlow and PyTorch.
• Proficiency in Python for developing and optimizing data analysis solutions.
• Hands-on experience with machine learning libraries like TensorFlow, PyTorch, or Hugging Face Transformers.
• Understanding of NLP concepts for working with Large Language Models.
• Familiarity with Generative AI techniques for creating new data samples, such as GANs (Generative Adversarial Networks) or VAEs (Variational Autoencoders).

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