Sr. Data Scientist

6sense•San Francisco, CA

5d•$162,924 - $238,955•Remote

About The Position

We're looking for a Senior Data Scientist (IC4) to join our growing AI and Data Science team. This is an exciting opportunity for an experienced ML practitioner who enjoys building production-grade AI systems and transforming cutting-edge research into impactful customer-facing products. You will work on advanced machine learning, NLP, LLMs, Agentic AI, and GenAI applications that directly influence product innovation and customer outcomes.

Requirements

8+ years of experience building and deploying machine learning solutions in production.
Strong foundation in Machine Learning, Statistics, and Applied Data Science.
Experience with: Supervised & Unsupervised Learning, Recommendation Systems, Ranking Models, Predictive Modeling.
Deep expertise in: NLP, Transformers, Embeddings, Encoder-Decoder Architectures, Retrieval-based Systems.
Hands-on experience with GenAI ecosystems including: LangGraph, LangChain, Amazon Bedrock.
Strong Python programming skills.
Experience building distributed ML systems and pipelines using AWS and Databricks.
Strong understanding of feature engineering, model evaluation, and MLOps.
Ability to independently solve ambiguous problems and drive execution.
Strong product mindset with focus on customer impact and business outcomes.

Nice To Haves

Experience building Agentic AI and Multi-Agent Systems.
Experience with: RAG Architectures, Vector Databases, Prompt Engineering.
Hands-on experience with PyTorch and/or TensorFlow.
Prior experience in B2B SaaS or customer-facing AI products.

Responsibilities

Design, build, and deploy scalable machine learning and AI solutions in production environments.
Develop NLP and transformer-based systems for enterprise-scale applications.
Build Agentic AI workflows leveraging frameworks such as LangGraph and LangChain.
Own the end-to-end ML lifecycle, including: Data exploration, Feature engineering, Model development, Evaluation, Deployment, Monitoring.
Develop ranking, recommendation, classification, prediction, and optimization models.
Partner closely with Product, Engineering, and Analytics teams to solve complex business problems.
Improve performance, scalability, reliability, and observability of production ML systems.
Contribute to AI platform architecture and technical strategy.
Mentor engineers and help elevate engineering excellence across the organization.