Job Title: Data Scientist Our client is looking for a modern data scientist who blends advanced analytical skills with hands-on experience in large language models, generative AI, and data engineering. This individual works closely with business leaders to frame use cases, prototypes rapidly, and builds scalable solutions that bridge insight and automation. Equally comfortable with notebooks and production pipelines, they are a full-spectrum contributor from idea to deployed intelligence. Length: 6-month Contract-to-hire Associate Vendors: We are accepting applications from candidates who are currently authorized to work in the US for any employer without sponsorship. Role & Responsibilities • Deep knowledge of LLMs (OpenAI, Claude, Cohere, LLaMA, etc.) and prompt engineering • Experience fine-tuning and embedding LLMs for domain-specific applications • Classical ML: regression, classification, time series, clustering, model evaluation • AWS stack preferred • Proficient in PySpark, SQL, dbt, and cloud-native pipelines (e.g., AWS Glue, Lambda, S3, Step Functions) • Strong ETL/ELT design skills; experience with data lakehouse and modern data stack • Familiar with orchestrators like Airflow or Dagster • Python (pandas, scikit-learn, LangChain, Hugging Face, Pydantic) • Jupyter, VS Code, Git, Docker • Experience deploying models via REST APIs or event-driven architectures • Skilled at scoping analytics and GenAI use cases from business questions • Builds POCs to validate value quickly, then scales to production • Great communicator who can explain trade-offs, model limitations, and data caveats to non-technical audiences • Cross-functional partner to product, engineering, and ops teams • Code-first mindset with a strong sense of reproducibility, testing, and observability • Advocates for responsible AI and MLOps best practices Required Qualifications: • 5+ years in Data Science • Expertise in Large Language Models (OpenAI, Claude, Cohere, LLaMA, etc.) • Well-versed in Prompt Engineering • Strong Machine Learning Fundamentals (regression, classification, time series, clustering, model evaluation) • Deep understanding of Generative AI and use cases in the world of Data Science • Strong in programming with tools like Python (PySpark), dbt, Jupyter, REST APIs • Strong ELT/ETL design background • Experience with Cloud Native data pipelines in AWS Desired Qualifications: • Strong AWS stack knowledge for AI/ML