Data Scientist

About PLACE

PLACE is a fast-growing, profitable startup operating at the junction of real estate, technology, business services, and consumer markets. On a trajectory toward an IPO, the company values high standards, agility, and excellence in execution.

Role Overview

As a Data Scientist at PLACE, you will independently manage end-to-end data science and machine learning projects. Working within a small, agile team, you'll contribute to pioneering products including computer vision, valuation modeling, and generative AI-powered search. Collaboration with your Manager and cross-functional colleagues will be key to advancing models from experimentation to robust production deployment, ongoing monitoring, and iterative enhancement.

Key Responsibilities

• Examine data rigorously to validate or challenge hypotheses, prioritizing evidence over biases.
• Select the most appropriate modeling techniques ranging from gradient boosting to transformer architectures.
• Develop, train, test, and fine-tune models, ensuring thorough validation of performance.
• Deploy production-grade models on reliable infrastructure to support real user interactions.
• Document modeling methodologies, testing procedures, and decision-making rationales for team transparency.
• Monitor model operations post-deployment and make timely improvements addressing drift or performance changes.
• Investigate agentic and reasoning-based AI systems, distinguishing practical innovations from hype in semi-autonomous technologies.
• Perform other assigned duties as necessary.

Required Qualifications and Experience

• Bachelor's degree or a comparable level of experience.
• At least three years of professional experience, including 3 to 5+ years directly working with AI technologies such as large language models (GPT, Claude, Qwen), and building/deploying machine learning or deep learning models.
• Proficiency in Python programming, with clean, production-standard coding skills.
• Solid experience with ML libraries and frameworks including PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, AutoGluon, and CatBoost.
• Familiarity with experiment tracking tools such as MLflow or Weights & Biases.
• Knowledge of model testing, evaluation, validation techniques and documentation standards.
• Experience with feature engineering, ML pipelines, and model serving methods (batch, real-time, and streaming).
• Strong SQL skills, especially with Snowflake, and competence handling large datasets.
• Comfortable using AWS services (Bedrock, SageMaker, Lambda, S3, EC2, Step Functions, CloudWatch, EKS), Docker, and infrastructure automation.
• Experience deploying and maintaining machine learning models in production environments with ongoing health monitoring.
• Familiarity with version control (Git) and collaborative tools such as Jira, Confluence, Slack, and Jupyter notebooks.

Preferred Additional Skills

• Experience in developing autonomous or semi-autonomous AI systems and knowledge of agent frameworks (Strands, AgentCore, LangChain) or reasoning methods (ReAct, chain-of-thought, MCP).
• Understanding of planning algorithms and decision-making frameworks under uncertainty.
• Expertise in computer vision tasks such as image classification, object detection, segmentation, and transfer learning.
• Industry knowledge in real estate, mortgage, financial services, or logistics including valuation modeling, risk scoring, and pricing algorithms.
• Exposure to time series forecasting or geospatial data analysis.
• Hands-on experience with ML CI/CD processes, model versioning, A/B testing, canary deployments, and drift monitoring.

Compensation and Benefits

The annual salary range for this role is $135,000 to $170,000, commensurate with experience.

PLACE believes in empowering employees to work from their preferred location—be it home, office, or while traveling. Benefits include flexible paid time off, comprehensive insurance, 401(k) matching, stock option grants, and a stock purchase plan. Every team member is a stakeholder, contributing to and benefiting from the company's growth.