Data Scientist

#Data Scientist #Data analysis #Retrieval-Augmented Generation (RAG) #Dataanalysis #EDA #

#NumPy #scikit-learn #pandas #NLP #NLR #FAISS #AWS #BERT #Python #scikit

Job Overview:

• Build, train, and validate machine learning models for prediction, classification, and clustering to support

NBA use cases.

• Conduct exploratory data analysis (EDA) on both structured and unstructured data to extract actionable

insights and identify behavioral drivers.

• Design and deploy A/B testing frameworks and build pipelines for model evaluation and continuous

monitoring.

• Develop vectorization and embedding pipelines using models like Word2Vec, BERT, to enable semantic

understanding and similarity search.

• Implement Retrieval-Augmented Generation (RAG) workflows to enrich recommendations by integrating

internal and external knowledge bases.

• Collaborate with cross-functional teams (engineering, product, marketing) to deliver data-driven Next Best

Action strategies.

• Present findings and recommendations clearly to technical and non-technical stakeholders.

Required Skills & Experience:

• Strong programming skills in Python, including libraries like pandas, NumPy, and scikit-learn.

• Practical experience with text vectorization and embedding generation (Word2Vec, BERT, SBERT, etc.).

• Proficiency in Prompt Engineering and hands-on experience in building RAG pipelines using LangChain,

Haystack, or custom frameworks.

• Familiarity with vector databases (e.g., PostgreSQL with pgvector, FAISS, Pinecone, Weaviate).

• Expertise in Natural Language Processing (NLP) tasks such as NER, text classification, and topic modeling.

• Sound understanding of supervised learning, recommendation systems, and classification algorithms.

• Exposure to cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes) is a plus.