Data Scientist

Summary

PLACE is a profitable, hypergrowth company operating at the intersection of real estate, technology, business services, and consumer products. The Data Scientist will own data science and machine learning initiatives end-to-end, including model development, production deployment, monitoring, and iteration across computer vision, valuation modeling, generative AI search, traditional machine learning, and agentic systems.

Responsibilities

• Analyze data to support or disprove a thesis, letting evidence guide conclusions over confirmation bias
• Select and implement the right tools for each problem, from gradient boosting models to transformer-based approaches
• Build, train, test, and validate models — from algorithm selection through hyperparameter tuning and rigorous evaluation
• Engineer models into production so they run reliably on real infrastructure, serving real customers
• Document models, testing protocols, and decision rationale for the team
• Monitor and improve models in production, knowing when to retrain, rebuild, or rethink as data and performance drift
• Explore agentic and reasoning systems, helping the team separate what's genuinely useful from hype in semi-autonomous, planning AI
• Other duties as assigned or apparent

Skills

• Bachelor's degree or equivalent experience
• 3+ years of prior work-related experience, including 3–5+ years of hands-on AI experience (LLMs like GPT, Claude, Qwen, or similar; building and deploying ML/DL models in production)
• Hands-on experience with PyTorch and/or TensorFlow, scikit-learn, XGBoost, LightGBM, AutoGluon, CatBoost, and experiment tracking (MLflow, Weights & Biases)
• Experience with model testing frameworks, evaluation, validation, and documentation
• Familiarity with ML pipelines, feature engineering, and model serving patterns (batch, real-time, streaming)
• Git and collaborative development practices; working familiarity with Jira, Confluence, Slack, and Jupyter
• Experience building autonomous or semi-autonomous AI systems; familiarity with agent frameworks (Strands, AgentCore, LangChain) or reasoning architectures (ReAct, chain-of-thought, MCP)
• Understanding of planning algorithms and decision-making under uncertainty
• Experience with image classification, object detection, or segmentation, and transfer learning
• Background in real estate, mortgage, financial services, or logistics (valuation models, risk scoring, pricing algorithms)
• Familiarity with time series forecasting or geospatial analysis
• Experience with CI/CD for ML, model versioning, A/B testing, canary deployments, and drift monitoring

Qualifications

Must Haves

• Bachelor's degree or equivalent experience
• 3+ years of prior work-related experience, including 3–5+ years of hands-on AI experience (LLMs like GPT, Claude, Qwen, or similar; building and deploying ML/DL models in production)
• Hands-on experience with PyTorch and/or TensorFlow, scikit-learn, XGBoost, LightGBM, AutoGluon, CatBoost, and experiment tracking (MLflow, Weights & Biases)
• Experience with model testing frameworks, evaluation, validation, and documentation
• Familiarity with ML pipelines, feature engineering, and model serving patterns (batch, real-time, streaming)
• Git and collaborative development practices; working familiarity with Jira, Confluence, Slack, and Jupyter

Nice to Haves

• Experience building autonomous or semi-autonomous AI systems; familiarity with agent frameworks (Strands, AgentCore, LangChain) or reasoning architectures (ReAct, chain-of-thought, MCP)
• Understanding of planning algorithms and decision-making under uncertainty
• Experience with image classification, object detection, or segmentation, and transfer learning
• Background in real estate, mortgage, financial services, or logistics (valuation models, risk scoring, pricing algorithms)
• Familiarity with time series forecasting or geospatial analysis
• Experience with CI/CD for ML, model versioning, A/B testing, canary deployments, and drift monitoring

Benefits

• A "work from the PLACE you work best" approach — at home, in an office, or on the move
• PTO as needed
• Comprehensive insurance coverage
• A 401(k) match
• Stock option grants
• A stock purchase plan