> Markdown version of [/jobs/ext/2435588-data-scientist-consultant](https://www.wearedevelopers.com/jobs/ext/2435588-data-scientist-consultant). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist (Consultant) - **Company:** Strategic Staffing Solutions - **Location:** Detroit, MI, United States - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon S3, Unit Testing, Cluster Analysis, Code Review, Information Engineering, Extract Transform Load (ETL), Python (Programming Language), PostgreSQL, Machine Learning, Metadata, Microsoft SQL Server, NumPy, Open Source Technology, Standard Sql, Search Technologies, Software Engineering, Systems Integration, Data Ingestion, Pytorch, Large Language Models, Prompt Engineering, Deep Learning, Generative AI, Git, Pandas, Data Lakes, Scikit Learn, Data Lineage, Api Design, Restful APIs, Data Pipelines, Docker, Unsupervised Learning - **Published:** August 8, 2026 - **Apply:** https://www.dice.com/job-detail/e73b1b3f-6a7c-4b5a-ada2-07638bbc2901 ## About the Role We are looking for a senior practitioner who has successfully built and deployed at least one production-grade Retrieval Augmented Generation (RAG) solution. The ideal candidate should be capable of working across the full AI lifecycle, including ingestion, retrieval, modeling, APIs, testing, deployment, monitoring, and operational support. The candidate should also have strong expertise in classical machine learning and statistics and be experienced in delivering explainable, auditable AI solutions within regulated, human-in-the-loop environments. Additional details: Machine Learning & Applied AI * Strong background in supervised and unsupervised learning, including: * Classification * Ranking * Clustering * Anomaly detection * Predictive modeling Experience selecting evaluation metrics and designing representative test datasets Hands-on experience with explainability techniques such as SHAP, LIME, and feature importance analysis Experience designing human-in-the-loop AI systems with review, escalation, override, and feedback mechanisms Generative AI & Retrieval * Production experience with: * RAG architectures * Embeddings * Semantic search * Re-ranking * Prompt engineering * Vector databases Experience building enterprise copilots, assistants, or document-grounded decision-support systems Ability to evaluate retrieval quality, grounding, hallucination risk, answer quality, and failure modes Experience with open-source or locally hosted LLMs preferred Python & Software Engineering * Advanced Python skills, including: * Pandas * NumPy * Scikit-learn * PyTorch (or similar deep learning framework) Ability to develop maintainable, production-quality code rather than notebook-only solutions Experience with: * Git and code reviews * Unit and integration testing * CI/CD pipelines * API development * Docker or Podman Experience with agentic development is a plus Comfortable working in both local and cloud environments Data Engineering & Integration * Strong SQL and ETL/ELT development skills * Experience integrating: * REST APIs * Enterprise document repositories * Workflow systems * Batch and incremental data pipelines Experience with PostgreSQL, SQL Server, data lake architectures, data lineage, and data quality controls Ability to design resilient ingestion pipelines for documents, metadata, attachments, and changing source-system records *Beware of scams. S3 never asks for money during its onboarding process ## Related Videos - [RAG's Not Dead, You're Just Using It Wrong! - Phil Nash](https://www.wearedevelopers.com/videos/1906-rag-s-not-dead-you-re-just-using-it-wrong-phil-nash) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [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) - [Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation](https://www.wearedevelopers.com/videos/1235-carl-lapierre-exploring-advanced-patterns-in-retrieval-augmented-generation) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [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) - [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) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)