Fullstack Senior Data Scientist

WhyHireWrong?•Capon Bridge, WV

4h•PLN 105 - PLN 170•Remote

About The Position

This is a data and analytics services company, founded in 2008, working with over 75 large consumer goods brands across 30 countries. The work covers AI and machine learning, supply chain analytics, customer analytics, data platforms, and digital commerce. Clients are big, the engagements are complex, and the expectations are enterprise-grade. The company is building and delivering GenAI solutions for large clients in retail, CPG, and manufacturing. They need someone who can both lead the technical direction of those projects and stay hands-on throughout the full build. This is not a research role and it is not a management-only role. You will make architectural decisions and also write the code. You will work directly with international clients, often translating complex technical choices into language that non-technical stakeholders can act on Engagements are with large enterprises in retail, CPG, and manufacturing. These clients have complex legacy environments, change management processes, and compliance requirements You will be responsible for both the architecture decisions and the code quality on the same project, with other engineers looking to you for direction The work involves production systems, not pilots. If something breaks in production for a major retail client, you are part of the response

Requirements

6 or more years in Data Science or AI engineering with a track record of shipping production systems, not just prototypes
4 or more years writing production-ready Python for AI and ML workloads: clean, maintainable, deployable code
2 or more years of hands-on production experience with LLM-based systems, including RAG pipelines, agent frameworks, and evaluation
The ability to make architectural decisions under uncertainty and defend them clearly to both technical and non-technical audiences
Fluent English for direct client communication

Responsibilities

Lead the design and discovery phase for GenAI projects: translate business problems into concrete architectures covering model selection, RAG, agents, fine-tuning, and guardrails
Build complete GenAI solutions end to end, covering data ingestion, retrieval layers, orchestration (LangChain, LlamaIndex, LangGraph), API and backend
Design RAG pipelines using vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks
Own prompting strategies, model selection, and fine-tuning approaches including LoRA, QLoRA, and supervised fine-tuning
Implement safety and governance controls: input/output filters, PII handling, audit logs, human-in-the-loop mechanisms
Gather technical requirements directly from client stakeholders and produce reliable delivery estimates
Mentor other data scientists and engineers on GenAI patterns, code quality, and best practices
Track the GenAI landscape actively and run targeted proof-of-concepts on emerging approaches