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# Data Scientist / AI Specialist (Junior to Mid-Level)

- **Company:** REFOCUS, INC.
- **Location:** United States (Remote available)
- **Experience:** Starter
- **Salary:** $90,000.0 - $110,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Computer Programming, Databases, Data Cleansing, Information Engineering, Relational Databases, Database Queries, Text Processing, Github, Python (Programming Language), Machine Learning, NLTK (NLP Analysis), NoSQL, NumPy, Open Source Technology, Sentiment Analysis, Systems Integration, Unstructured Data, Freeform SQL, Feature Engineering, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Software Application Programming, Kaggle, Generative AI, Pandas, Scikit Learn, Information Technology, HuggingFace, Machine Learning Operations, Spacy, Docker, Unsupervised Learning, Web Api
- **Published:** August 24, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=924529933cf335d0

## About the Role

Education: Bachelor's or Master's degree in Data Science, Computer Science, Artificial Intelligence, Statistics, or a related field.

Core Programming: Advanced proficiency in Python and essential data science packages (Pandas, NumPy, Scikit-Learn).

AI & NLP Expertise: Hands-on experience with NLP libraries (NLTK, SpaCy, Hugging Face Transformers) and modern AI techniques.

LangChain & LLMs: Practical knowledge of building applications with LangChain, prompt engineering, vector databases (e.g., Pinecone, Chroma, FAISS), and OpenAI/Open-source LLM APIs.

Database & Querying: Solid experience writing complex SQL queries for relational databases, as well as working with NoSQL or document databases.

Mathematics & ML Fundamentals: Deep understanding of statistical analysis, probability, supervised/unsupervised learning algorithms, and model evaluation metrics.

Preferred

Familiarity with MLOps tools (MLflow, Docker) for model deployment and monitoring.

Experience with cloud platforms (AWS, GCP, or Azure).

Active portfolio featuring AI/NLP projects, Kaggle competitions, or open-source GitHub contributions.

## Description

AI & NLP Solutions: Design, implement, and fine-tune NLP models, sentiment analysis pipelines, and text processing workflows.

LLM & Generative AI Applications: Utilize frameworks like LangChain to build autonomous agents, retrieval-augmented generation (RAG) pipelines, and integrate Large Language Models (LLMs) into production workflows.

ML Model Development: Develop, validate, and deploy end-to-end Machine Learning models for predictive analytics, classification, and clustering tasks.

Data Engineering & EDA: Perform exploratory data analysis, feature engineering, and data cleaning across structured and unstructured datasets.

System Integration: Work closely with cross-functional engineering teams to integrate ML and AI models into API endpoints and client applications.


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