Data Scientist

Compunnel

$138K — $165K *
Information Technology
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • 10+ years of Data Science experience.
  • 5+ years of hands-on experience with Generative AI, LLMs, and RAG.
  • Strong experience in deploying production-grade machine learning systems.
  • Proficiency in Python programming for AI applications.
  • Solid background in NLP and transformer-based architectures.
  • Experience with distributed computing systems like Spark or Hadoop.
  • Expertise mentoring junior data scientists.

Responsibilities

  • Design and develop customer-facing Generative AI products.
  • Build and optimize RAG pipelines and semantic retrieval systems.
  • Evaluate and improve production-grade machine learning systems.
  • Implement LLM evaluation frameworks and continuous optimization.
  • Work closely with Machine Learning Engineers in product deployment.
  • Optimize transformer-based NLP models.
  • Analyze large datasets using distributed computing platforms.

Benefits

  • Opportunity to lead AI initiatives and transition into a Lead Data Scientist role.
  • Collaboration with product, engineering, and business teams.
  • Mentoring junior team members.
  • Engagement in cutting-edge technology with Generative AI and LLMs.
  • Access to advanced tools and platforms for data analysis.
Full Job Description
Job Summary:

We are seeking a Senior Data Scientist with deep expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and production-scale machine learning systems. This role focuses on building and deploying customer-facing AI products, leading RAG implementations, evaluating LLM performance, and scaling production-grade ML systems. The ideal candidate will bring 10+ years of data science experience, including at least 5 years of hands-on GenAI experience, with the intent to transition into a Lead Data Scientist role.

Key Responsibilities:
• Design, develop, and deploy customer-facing Generative AI and LLM-powered products.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines and semantic retrieval systems.
• Develop and evaluate production-grade machine learning systems at scale.
• Implement LLM evaluation frameworks, benchmarking, validation, and continuous model improvement.
• Work closely with Machine Learning Engineers throughout the product development and deployment lifecycle.
• Design and optimize transformer-based NLP solutions using models such as BERT, RoBERTa, and T5.
• Apply advanced prompt engineering and model optimization techniques for production applications.
• Analyze large datasets using distributed computing platforms such as Spark and Hadoop.
• Mentor junior data scientists and provide technical leadership on AI initiatives.
• Collaborate with product, engineering, and business teams to deliver scalable AI solutions.

Required Qualifications:
• 10+ years of Data Science experience.
• 5+ years of recent hands-on experience with Generative AI, LLMs, RAG, and Agentic AI.
• Strong experience building and deploying production-grade ML systems at scale.
• Strong LLM and Generative AI deployment experience, including implementation and evaluation.
• Experience following the complete product deployment lifecycle, including product build, maintenance, and new feature development.
• Experience working alongside Machine Learning Engineers through product deployment.
• Strong Python programming skills.
• Strong background in NLP and transformer-based architectures.
• Experience working with large datasets and distributed computing systems such as Spark or Hadoop.
• Experience with machine learning algorithms including deep learning, gradient boosting, and random forests.
• Experience mentoring, training, and serving as a subject matter expert.

Preferred Qualifications:
• Experience with ChatGPT, GPT-3.5, Claude, Mistral, or similar LLM platforms.
• Experience transitioning into technical leadership or Lead Data Scientist responsibilities.
• Experience building customer-facing AI products in enterprise environments.

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