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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - **Company:** Phaedon, Llc - **Location:** Minneapolis, MN, United States - **Experience:** Expert - **Salary:** $105,000.0 - $155,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Build Automation, Software as a Service, Cloud Computing, Computer Programming, Computer Engineering, Continuous Integration, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Object-Oriented Software Development, Recommender Systems, Power BI, SQL Databases, Tableau (Software), Management of Software Versions, Data Logging, Pulumi, Feature Engineering, Chatbots, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Boto3, Git, Cloudformation, AI Platforms, Scikit Learn, Integration Tests, Information Technology, Deployment Automation, Machine Learning Operations, Api Design, Terraform, Software Version Control, Data Pipelines, Docker, Microservices - **Published:** July 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=6891f17a3e96ad61 ## About the Role We're looking for a self-starter who identifies opportunities to apply AI/ML to the product roadmap, proposes the approach, builds it, ships it, and owns it in production. The primary focus of this role is product-embedded model development. There will be some client-facing work; however, it is anticipated to be a small portion of the role., * Bachelor's degree in data science, computer science, computer engineering, or related field AND 5+ years of hands-on experience building and shipping ML models into production systems OR equivalent combination of education and experience * Demonstrated track record of taking a model from idea to production-serving endpoint inside a live product, not just research/POC work; be prepared to speak to specific systems you built that are running in production today * Fluency in the full model lifecycle: data/feature engineering, training, evaluation, deployment, versioning, monitoring, and retraining * Knowledge of Infrastructure-as-Code (Terraform, Pulumi, CloudFormation) for deploying ML infrastructure repeatably * Experience with source control and automated deployment pipelines (Git, Docker) * A demonstrated self-starter mindset: comfortable identifying a product opportunity, scoping the technical approach, and driving it to completion with minimal guidance * Strong written and verbal communication skills to document and present technical approaches to engineering and product stakeholders Technical Skills: * Programming: Advanced Python (including AI/ML libraries like transformers, LangChain), SQL, Boto3 * AI/ML Tools: AWS Bedrock, SageMaker, prompt engineering, model fine-tuning * Cloud Services: AWS services, particularly Bedrock, SageMaker, Lambda, Redshift, Athena, and Glue * Visualization: Experience with Superset, Tableau, and/or Power BI * Development Practices: Object-oriented programming, testing frameworks, CI/CD, model versioning Preferred Skills: * Direct experience building models that are embedded in and called by a live SaaS product (recommendation engines, fraud/anomaly detection, personalization, forecasting, chatbots) * Experience with vector databases and RAG implementations in production * Knowledge of LLM fine-tuning, evaluation, and deployment strategies at scale * Strong MLOps background: model versioning, automated retraining, drift detection, canary/shadow deployments * Experience with API development and microservices architecture in a product engineering context * Background in fraud detection, loyalty/rewards platforms, or marketing/AdTech modeling a plus * Prior experience balancing product engineering with occasional client-facing technical work ## Description We are looking for a Senior Data Scientist who is a builder, not just a maintainer. This is a high-ownership opportunity for an AI/ML engineer who wants to design and ship the models that power our loyalty platform in production, not just prototype them. You'll build AI/ML capabilities that our SaaS product calls at runtime: fraud detection, personalization, recommendation, and forecasting models served through APIs, not one-off notebooks handed to someone else to productionize., Product-Embedded Model Development (primary focus): * Design, build, and own AI/ML models that are directly integrated into and called by our SaaS product in production * Own the full model lifecycle: problem framing, data/feature design, training, evaluation, deployment as a callable service, and post-deploy monitoring/retraining * Build and maintain production inference APIs and microservices that serve model predictions to the product with defined latency and reliability SLAs * Implement and productionize models using AWS Bedrock, SageMaker, and other AWS AI services, going beyond POC into hardened, versioned, production systems * Develop RAG (Retrieval-Augmented Generation) systems and other LLM-powered features as first-class product capabilities * Proactively identify where AI/ML can create product differentiation (fraud detection, member behavior prediction, personalization/recommendation, anomaly detection) and bring proposals forward rather than waiting for requirements to be handed down Cloud Infrastructure & MLOps: * Build and manage SageMaker training pipelines, model registry, and endpoint deployments, including feature store integration and automated retraining triggers * Build automation, monitoring, and alerting for production ML systems using Lambda and other AWS services * Create and maintain Infrastructure-as-Code (Terraform, Pulumi, CloudFormation) for all model and pipeline infrastructure, no manual, undocumented deployments * Build data pipelines that synthesize complex datasets from multiple sources into model-ready features * Develop CI/CD pipelines for automated deployment and model versioning; implement model registry and rollback practices * Implement error-proofing, integration testing, and monitoring/logging for AI systems running in production Client & Cross-Functional Collaboration: * Support select client engagements where deep technical model expertise is needed to scope or validate an AI/ML approach * Partner with product and analytics leadership to translate roadmap priorities into shipped model capabilities * When client-facing, present technical findings and recommendations with clarity to both technical and business stakeholders ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [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) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [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) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)