Data Scientist - AI

Summary

Internet Brands is a fully integrated online media and software services company headquartered in El Segundo, California. They are seeking a Data Scientist to work on personalization initiatives, focusing on machine learning and deep learning to enhance customer experience and business intelligence capabilities.

Responsibilities

• Processing, cleansing, and verifying the integrity of data used for analysis
• Doing ad-hoc analysis and presenting results in a clear manner
• Experience with feature synthesis and selection techniques to build models
• Solid understanding of regression, classification algorithms framework
• Apply machine learning techniques at scale on massive data sets
• Working with large language models (LLMs) for applications such as content summarization, recommendation, and personalization. Apply RAG (Retrieval-Augmented Generation) pipelines for knowledge retrieval
• Experiment agentic AI framework to enhance personalization and automation
• Prior work with model evaluation benchmarks and large-scale LLM testing frameworks
• Experience with prompt fine-tuning techniques, LLMs optimization techniques(pre-training, post-training etc)
• Collaborate with cross-functional agile teams of software engineers, domain experts, and others to build new product features for multiple business units
• Collaborate with data scientists across a variety of businesses to prioritize and promote our machine learning efforts

Skills

• 2-4 years of data science experience with expertise in predictive analytics
• B.S., M.S. or PhD in Data Science, Computer Science, Software Engineering, Information Science, Mathematics, Statistics, Electrical Engineering, Physics or related fields or equivalent experience
• Strong understanding of transformer-based architectures, embeddings, and tokenization techniques
• Proficiency with LLM frameworks and APIs, such as LangChain, Hugging Face Transformers, OpenAI, Google Vertex AI (Gemini), or Anthropic Claude, coupled with expertise in evaluation and performance benchmarking
• Demonstrated ability to implement prompt engineering, fine-tuning, and evaluation for LLMs
• Strong understanding of personalization and recommendation algorithms, including collaborative filtering (CF), content-based filtering, hybrid recommenders, sequence-based or session-based models, and graph-based models
• Proficiency in using query languages such as SQL
• Experience with common data science toolkits, such as R, Scikit-learn, NumPy, Tensorflow
• Solid understanding of regression, classification algorithms framework
• Experience with feature synthesis and selection techniques to build models
• Apply machine learning techniques at scale on massive data sets
• Working with large language models (LLMs) for applications such as content summarization, recommendation, and personalization
• Apply RAG (Retrieval-Augmented Generation) pipelines for knowledge retrieval
• Experiment agentic AI framework to enhance personalization and automation
• Prior work with model evaluation benchmarks and large-scale LLM testing frameworks
• Experience with prompt fine-tuning techniques, LLMs optimization techniques (pre-training, post-training etc)
• Collaborate with cross-functional agile teams of software engineers, domain experts, and others to build new product features for multiple business units
• Collaborate with data scientists across a variety of businesses to prioritize and promote our machine learning efforts

Qualifications

Must Haves

• 2-4 years of data science experience with expertise in predictive analytics
• B.S., M.S. or PhD in Data Science, Computer Science, Software Engineering, Information Science, Mathematics, Statistics, Electrical Engineering, Physics or related fields or equivalent experience
• Strong understanding of transformer-based architectures, embeddings, and tokenization techniques
• Proficiency with LLM frameworks and APIs, such as LangChain, Hugging Face Transformers, OpenAI, Google Vertex AI (Gemini), or Anthropic Claude, coupled with expertise in evaluation and performance benchmarking
• Demonstrated ability to implement prompt engineering, fine-tuning, and evaluation for LLMs
• Strong understanding of personalization and recommendation algorithms, including collaborative filtering (CF), content-based filtering, hybrid recommenders, sequence-based or session-based models, and graph-based models
• Proficiency in using query languages such as SQL
• Experience with common data science toolkits, such as R, Scikit-learn, NumPy, Tensorflow
• Solid understanding of regression, classification algorithms framework
• Experience with feature synthesis and selection techniques to build models
• Apply machine learning techniques at scale on massive data sets
• Working with large language models (LLMs) for applications such as content summarization, recommendation, and personalization
• Apply RAG (Retrieval-Augmented Generation) pipelines for knowledge retrieval
• Experiment agentic AI framework to enhance personalization and automation
• Prior work with model evaluation benchmarks and large-scale LLM testing frameworks
• Experience with prompt fine-tuning techniques, LLMs optimization techniques (pre-training, post-training etc)
• Collaborate with cross-functional agile teams of software engineers, domain experts, and others to build new product features for multiple business units
• Collaborate with data scientists across a variety of businesses to prioritize and promote our machine learning efforts

Benefits

• Health insurance options such as medical, dental, and vision coverage
• Flexible spending accounts (FSA) for medical and dependent care
• Short-term and long-term disability insurance
• Life and AD&D insurance
• 401(k) retirement savings plan with a company match
• Paid time off (PTO)
• Paid holidays
• Commuter benefits
• Access to our Employee Assistance Program (EAP)
• Well-being coaching services
• Voluntary benefits such as home, auto and pet insurance
• Discounted legal and financial services