# nineDots.io (client undisclosed) — Principal Data Scientist

| Field | Value |
|---|---|
| **Date found** | 2026-05-21 |
| **Company** | nineDots.io (client: undisclosed enterprise software company) |
| **Role** | Principal Data Scientist |
| **Location** | Dublin, Hybrid (frequency unspecified) |
| **Salary** | Up to €130k base + enterprise benefits |
| **Job URL** | [LinkedIn](https://www.linkedin.com/jobs/view/4412000630/) |
| **Status** | New |

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## Company Research

Recruiting agency (nineDots.io) — end client undisclosed.

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## Job Summary

**What they do:** Client is an unnamed globally established enterprise software company with a commercially mature platform used internationally.

**The role:** Principal Data Scientist — senior technical authority within the Dublin AI function, deeply hands-on architect and production builder.

**Core work:**
- Design and build LLM applications, RAG pipelines, and agentic AI systems
- Lead initiatives from research through to production deployment
- Mentor senior engineers and partner with product leadership on the AI roadmap

**Stack:** Python · LLMs · RAG · agentic frameworks · MLOps / ML infrastructure

**Work style:** Dublin Hybrid (on-site frequency unspecified; typical Irish hybrid is 2–3 days/week — must clarify)

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## Score: 54%

| Dimension | Score | Justification |
|---|---|---|
| Agentic AI depth (25%) | 55% | "Agentic AI, agentic frameworks, and agent orchestration" explicitly listed alongside RAG, GenAI, and NLP — a genuinely broad AI principal role, but not a dedicated agentic AI engineering position |
| Tech fit (25%) | 60% | LLMs ✅, RAG ✅, agentic frameworks ✅, MLOps/ML infra ✅. No specific mention of LangChain/LangGraph/CrewAI, PyTorch, or Anthropic APIs |
| Remote fit (25%) | 40% | Dublin Hybrid with no frequency specified. Standard Irish hybrid is 2–3 days/week which would exceed the 1–2/month limit. Needs explicit clarification before applying |
| Company culture fit (15%) | 50% | "Enterprise stability + genuine AI ambition" — large established software company, real AI investment, but not AI-native. Client remains anonymous |
| IC/leadership balance (10%) | 80% | "True principal-level, deeply hands-on" with mentoring of senior engineers. No people management stated |
| **Final (weighted)** | **54%** | |

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## Strengths

- Agentic AI and agent orchestration explicitly in scope — partial alignment with career direction
- Principal-level IC with full technical authority, hands-on emphasis
- Salary stated: up to €130k — above the €110k floor, clearly communicated
- RAG pipelines + LLMs + agentic frameworks covers meaningful stack overlap
- Research → experimentation → production ownership model — exactly how Luca works
- Dublin-based (no relocation), and likely part of a well-funded AI investment area

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## Weaknesses & Risks

- **Hybrid frequency unknown** — the most critical blocker. "Hybrid" in Dublin usually means 2–3 days/week which fails the location hard filter. Must confirm before applying
- **Client anonymous** — actual employer, culture, and working environment unknown
- **Not AI-native** — "globally established enterprise software company" is a large legacy platform with an AI overlay, not an AI-first company
- **Broad AI scope** — agentic AI is one of several areas; this is a general AI/ML principal role, not a focused agentic engineering position
- **"Up to" salary framing** — €130k is the ceiling, not the floor. Actual offer may be lower depending on experience bracket
- **Recruiting agency layer** — nineDots.io adds friction and indirection; less direct relationship with the employer

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## Suggestions

- **Verify hybrid frequency before anything else** — if it's 2+ days/week the location filter fails; ask Lee Kelly at nineDots directly
- **Ask who the actual employer is** early in the process — anonymous client is a significant unknown
- Clarify whether €130k is the realistic target or a ceiling with lower typical offers
- If pursuing, position the Fenergo agentic pipeline and cybersecurity multi-agent platform as direct evidence of the "agentic frameworks and agent orchestration" requirement
- The RAG medical knowledge base (5,000+ papers, 43% retrieval improvement) is directly relevant to "RAG pipelines" requirement
- Not a top-tier match for agentic AI growth, but a credible fallback if hybrid frequency is acceptable

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## Interview Tracker

| Stage | Date | Notes |
|---|---|---|
| Applied | | |
| Recruiter screen | | |
| Technical interview | | |
| Final round | | |
| Offer / Outcome | | |