Applied Data Scientist (Spring)

Job Title: AI Data ScientistJob Type: Full-Time/Direct HirePay Range: $300,000–$350,000+ (Includes Salary, Bonus, Stocks, and RSUs) + BenefitsStart Date: ASAPLocation: Hybrid (3 days onsite in San Francisco, CA)About the Opportunity:
Our client, a leader in AI-Native Enterprise SaaS, is building a next-generation sales and talent intelligence platform serving enterprise customers (e.g., CHROs and large public companies). We are looking for a skilled AI Data Scientist to join their team and contribute to a critical, greenfield AI initiative. This project involves completely rebuilding the core platform from the ground up with a modern, AI-first architecture, free from legacy constraints. This is a high-impact role that requires a self-motivated professional who can hit the ground running and deliver results quickly.Key Responsibilities & Deliverables:
This role is focused on the successful completion of specific tasks and deliverables. Your responsibilities will include:
Model Development: Build and maintain ML models for classification, extraction, trend detection, and predictive scoring on large structured and unstructured datasets.Experimentation: Design experiments and benchmarks to measure model accuracy, reduce bias, and validate outputs at scale.NLP Application: Apply advanced Natural Language Processing (NLP) techniques—including embeddings, Named Entity Recognition (NER), and text classification—to real-world data pipelines.Production Ownership: Partner with engineering to move models from experimentation to production; own monitoring and drift detection.Evaluation: Build robust evaluation frameworks for AI-generated outputs across multiple product use cases.Required Skills & Experience:
We are looking for someone with a proven track record of successful production deployment. The ideal candidate will have:
Experience: 3–5 years of applied data science, with a minimum of 2 years working directly with NLP or large-scale text data in a production environment.Education: BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field.Technical Stack: Strong proficiency in Python (including pandas, scikit-learn, PyTorch or TensorFlow) and proficient in SQL.Model Deployment: Demonstrated track record of shipping models into production—not just producing analysis. This isn't a learning role—you need to be a subject matter expert.Expertise: Experience with embedding models and semantic similarity at enterprise scale.Authorization: Must be authorized to work in the US without current or future employer sponsorship.Soft Skills: Strong communication skills to provide clear and concise status updates to the project team, and the demonstrated ability to work autonomously and manage your own time effectively to meet project goals.
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