Data Scientist

Interview: Virtual
Visa: USC and GC
Hybrid: Houston, TX (local preferred)


JD:
We are seeking a curious, proactive, and innovative Data Scientist with a strong foundation in AI/ML and Large Language Models (LLMs) to join our team. The ideal candidate has experience blending various datasets, building statistical/machine learning models, and deploying AI-driven solutions that drive business impact.


This role involves working with LLMs, natural language processing (NLP), and deep learning techniques to develop AI-powered applications. You will play a pivotal role in designing, training, and deploying scalable AI/ML models, while also translating complex data insights into actionable business strategies.



Key Responsibilities:

• I/ML Model Development: Design, train, and fine-tune machine learning and deep learning models, including LLMs, for predictive analytics, automation, and AI-driven decision-making.
• Data Analysis & Feature Engineering: Collect, process, and analyze structured and unstructured data, engineering relevant features to improve model performance.
• gent-Based & NLP Applications: Develop LLM-based AI solutions with a focus on prompt engineering, fine-tuning, and inference optimization.
• Business Impact & Decision Support: Translate complex data science methodologies into actionable insights, collaborating with stakeholders to drive business value.
• End-to-End Model Deployment: Work with MLOps best practices to deploy and monitor models in production, ensuring scalability, efficiency, and reliability.
• Data Storytelling & Visualization: Develop clear, compelling presentations and dashboards to communicate findings to non-technical stakeholders.

Requirements:
Must Have: Snowflake, SQL, and Palantir experience (or similar data decision Operational AI).


Technical Skills:

• I & Machine Learning: Experience in predictive modeling, NLP, deep learning, and LLM-based applications (e.g., GPT, BERT, LangChain).
• Programming: Proficiency in Python and experience with AI/ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face).
• Data Engineering & SQL: Ability to write efficient SQL queries to blend and structure data from multiple sources for modeling and analysis.
• Cloud & MLOps: Experience with AWS (SageMaker, S3, Redshift), Snowflake, and ML pipeline automation.
• Version Control & Collaboration: Proficiency using Git for code versioning and teamwork.

Soft Skills:

• Curious & Innovative: Passionate about solving complex business problems using data and AI.
• Ownership & Initiative: Proactively drive projects from conception to deployment.
• Business Acumen: Understand how AI/ML solutions impact business goals and decision-making.
• Effective Communication: Ability to explain technical models and AI methodologies to non-technical audiences.

Preferred Qualifications:

• Graduate degree (Master's or Ph.D.) in a quantitative field (e.g., Computer Science, Data Science, Statistics, Engineering, Mathematics, Economics).
• Experience with dashboarding tools (e.g., Power BI, Dash, Streamlit) for model performance monitoring.
• Familiarity with reinforcement learning and AI agent-based applications.