The Value Maximizer
Data Scientist -
Key Responsibilities
Build and optimize classification, regression, and forecasting models using classical ML and deep learning techniques Develop and deploy deep learning architectures including LSTMs, transformers, and other sequence-based models for time-series, NLP, and anomaly detection Design and implement NLP pipelines for text classification, semantic search, summarization, and question answering using transformer-based models (e.g., BERT, T5, GPT) Create RAG (retrieval-augmented generation) pipelines integrating LLMs with vector databases (e.g., FAISS, Pinecone, Weaviate) and document indexing frameworks Apply and fine-tune LLMs (e.g., OpenAI, Mistral, LLaMA, Cohere) for domain-specific tasks using supervised fine-tuning or LoRA/QLoRA methods Build and orchestrate multi-agent AI systems using frameworks like LangGraph, CrewAI, or OpenAgents to support tool-using, autonomous agents for decision-making workflows Collaborate with data engineers, product managers, and stakeholders to translate business needs into production-ready solutions Mentor and support junior data scientists through code reviews, model design feedback, and collaborative experimentation Promote best practices in reproducible modeling, responsible AI, and scalable deployment
Required Skills & Experience
5+ years of experience in data science or applied machine learning, with a strong background in both classical and deep learning methods Hands-on experience with Python, and libraries/frameworks such as scikit-learn, pandas, PyTorch, TensorFlow, Hugging Face Transformers, and LangChain Strong understanding of classification metrics, feature engineering, model validation, and hyperparameter tuning Demonstrated experience with LLMs, including fine-tuning, prompt engineering, and retrieval-augmented generation techniques
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Key Responsibilities
Build and optimize classification, regression, and forecasting models using classical ML and deep learning techniques Develop and deploy deep learning architectures including LSTMs, transformers, and other sequence-based models for time-series, NLP, and anomaly detection Design and implement NLP pipelines for text classification, semantic search, summarization, and question answering using transformer-based models (e.g., BERT, T5, GPT) Create RAG (retrieval-augmented generation) pipelines integrating LLMs with vector databases (e.g., FAISS, Pinecone, Weaviate) and document indexing frameworks Apply and fine-tune LLMs (e.g., OpenAI, Mistral, LLaMA, Cohere) for domain-specific tasks using supervised fine-tuning or LoRA/QLoRA methods Build and orchestrate multi-agent AI systems using frameworks like LangGraph, CrewAI, or OpenAgents to support tool-using, autonomous agents for decision-making workflows Collaborate with data engineers, product managers, and stakeholders to translate business needs into production-ready solutions Mentor and support junior data scientists through code reviews, model design feedback, and collaborative experimentation Promote best practices in reproducible modeling, responsible AI, and scalable deployment
Required Skills & Experience
5+ years of experience in data science or applied machine learning, with a strong background in both classical and deep learning methods Hands-on experience with Python, and libraries/frameworks such as scikit-learn, pandas, PyTorch, TensorFlow, Hugging Face Transformers, and LangChain Strong understanding of classification metrics, feature engineering, model validation, and hyperparameter tuning Demonstrated experience with LLMs, including fine-tuning, prompt engineering, and retrieval-augmented generation techniques
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