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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Place, Inc. - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $135,000.0 - $170,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Data Analysis, Computer Vision, Confluence, JIRA, Big Data, Continuous Integration, Python (Programming Language), Machine Learning, Object Detection, Tensorflow, Software Deployment, SQL Databases, Data Streaming, Reinforcement Learning, Feature Engineering, Pytorch, ReactJS, Transfer Learning, Large Language Models, Snowflake, Multi-Agent Systems, Prompt Engineering, State Machines, Deep Learning, Generative AI, Jupyter, Git, Scikit Learn, Slack, Xgboost, Machine Learning Operations, Functional Programming, Cloudwatch, GPT, Software Version Control, Docker, Unsupervised Learning - **Published:** July 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=94d6622d48d77919 ## About the Role 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., * 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 ## Description 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., * 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 ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Collaboration Quantified: Lessons from Open Source Developer Networks](https://www.wearedevelopers.com/videos/1422-collaboration-quantified-lessons-from-open-source-developer-networks) - [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter) - [Streaming AI Responses in Real-Time with SSE in Next.js & NestJS](https://www.wearedevelopers.com/videos/1630-streaming-ai-responses-in-real-time-with-sse-in-next-js-nestjs) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Got AI ideas but no money? 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