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Data Scientist at PLACE Corporate Careers. Location Information: . 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. As our Data Scientist, you'll own data science and machine learning work end-to-end, with minimal oversight, on a small, agile team where computer vision, valuation modeling, generative AI-powered search, traditional ML, and agentic/reasoning systems are core to the product. You'll partner closely with your Manager, Data Science, and collaborate across a tight-knit team to take models from analysis through production deployment, monitoring, and iteration. If you thrive in complexity, entrepreneurial problem-solving, and believe in scaling through tech and AI, this is your PLACE.. . What You're Great At. You form hypotheses and let evidence guide your conclusions — you value intellectual honesty over confirmation bias, and you can explain uncertainty clearly rather than hiding behind surface-level metrics. You know when to reach for a well-tuned gradient boosting model and when a transformer-based approach is the right call, and you have a strong scientific foundation in linear algebra, calculus, probability, and statistical inference to back that judgment up.. . You've worked hands-on with LLMs via API/SDK, and you understand prompt engineering, RAG architectures, fine-tuning, and embedding models well enough to evaluate outputs critically and design real guardrails. You're comfortable across supervised and unsupervised learning — regression, classification, clustering, dimensionality reduction, ensemble methods — and deep learning, including CNNs, RNNs/LSTMs, transformers, and attention mechanisms. You've implemented reinforcement learning approaches (Q-learning, policy gradients, actor-critic, or multi-armed bandits) and understand reward shaping and the exploration/exploitation tradeoff.. . You write clean, production-quality Python, you're strong in Snowflake/SQL and comfortable with large datasets, and you know your way around AWS (Bedrock, SageMaker, Lambda, S3, EC2, Step Functions, CloudWatch, EKS), Docker, and infrastructure-as-code. You've deployed models to production and kept them healthy over time — not just shipped and walked away.. . What You'll Do. . . 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. . . What You'll Need. . . 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. . . Compensation:. $135,000–$170,000, depending on experience. . Why PLACE. We believe people do their best work when they're trusted, supported, and surrounded by others who are equally driven. That's why this role includes a "work from the PLACE you work best" approach — at home, in an office, or on the move. Our competitive benefits include PTO as needed, comprehensive insurance coverage, a 401(k) match, stock option grants, and a stock purchase plan. Every team member is an owner, building the "PLACE" they are proud to call "my company."