Data Scientist (Consultant)

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