Data Scientist
Core
End-to-end ownership of data science and machine learning work, including computer vision, valuation modeling, generative AI search, and agentic/reasoning systems.
Role type
Senior IC machine learning engineer (real estate & AI)
Builds
Production ML models, agentic systems, and AI-powered search features for real estate and business services
Domain
Real estate, technology, business services, consumer
Deliverable
production ML models
Required skills
hypothesis formulation, gradient boosting, transformer-based approaches, LLMs via API/SDK, prompt engineering, RAG architectures, fine-tuning, embedding models, supervised/unsupervised learning, deep learning (CNNs, RNNs/LSTMs, transformers), reinforcement learning, Python, Snowflake/SQL, AWS (Bedrock, SageMaker, Lambda, S3, EC2, Step Functions, CloudWatch, EKS), Docker, infrastructure-as-code, model monitoring and retraining
Preferred skills
autonomous/semi-autonomous AI systems, agent frameworks (Strands, AgentCore, LangChain), reasoning architectures (ReAct, chain-of-thought, MCP), planning algorithms, image classification, object detection, segmentation, transfer learning, time series forecasting, geospatial analysis, CI/CD for ML, model versioning, A/B testing, canary deployments, drift monitoring
Technologies
PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, AutoGluon, CatBoost, MLflow, Weights & Biases, Jira, Confluence, Slack, Jupyter
Responsibilities
Analyze data to support or disprove a thesis; select and implement appropriate ML tools; build, train, test, and validate models; engineer models into production; document models and decision rationale; monitor and improve models in production; explore agentic and reasoning systems
Seniority
Senior, hands-on IC