Data Scientist

About the role

Your Opportunity At PLACE, we're building a category-defining company at the intersection of real estate, technology, business services, and the consumer. As a profitable, hypergrowth startup, on the path to an IPO, our standards are high, our team is scrappy, and our commitment is to execute the best work of our lives. This is YOUR CHANCE to shape the backbone of a company that's scaling rapidly and innovating boldly.

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 AIOther duties as assigned or apparent

Requirements

• 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

Qualifications

• 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

Skills

• Clean, production-quality Python
• Strong scientific foundation in linear algebra, calculus, probability, and statistical inference
• Expertise in LLMs via API/SDK, prompt engineering, RAG architectures, fine-tuning, and embedding models
• Hands-on experience with supervised and unsupervised learning, deep learning, reinforcement learning, and model serving patterns
• Experience with AWS (Bedrock, SageMaker, Lambda, S3, EC2, Step Functions, CloudWatch, EKS), Docker, and infrastructure-as-code
• Deployed models to production and kept them healthy over time

Benefits

• Paid time off as needed
• Comprehensive insurance coverage
• 401(k) match
• Stock option grants
• Stock purchase plan

Pay

$135,000–$170,000, depending on experience

Schedule

Work from the PLACE you work best — at home, in an office, or on the move.