---
title: 'AI Analyst at Neurons Lab'
canonical: 'https://feeny.ai/job/ai-analyst-neurons-lab-poland-q2mt5mxp7x2h'
type: 'job'
last_seen: '2026-09-08'
---

# AI Analyst at Neurons Lab

- **Company:** Neurons Lab
- **Location:** Poland
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-07-30
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/neurons-lab.com/ba14425c-9051-4dce-85c3-839b425151c9

## Job description

ABOUT THE PROJECT (DESCRIPTION, DURATION, STAGE)

Join Neurons Lab as the AI Analyst on a flagship engagement with a European private investment group — a holding company with a C-level executive team, an investment/portfolio function and an affiliated family office.

The programme builds one private, access-scoped context layer over the group's data and then AI skills and agents on top of it — first for the executive team (6–10 people holding ~90% of group priorities), then for every employee. Two loops run on the same layer: alignment — are we doing the right things (strategy, OKRs, drift by team and by person) — and efficiency — are we doing things right (a process miner reads real workflows from the digital footprint, optimizer agents then implement the fixes).

Your half of the programme is the part that only a human can do. Phase 3 (Distill) is yours: sitting with each executive and getting what is in their head into the layer — group strategy and OKRs from the Chief of Staff, investment policy and portfolio base from the CIO, reporting standards and operating processes from the CFO and COO. And in the efficiency loop, you turn raw mining output into a written optimisation report per team: automate, reorganise, or leave alone — with the financial case attached.

Four phases — Capture → Connect → Distill → Build — over roughly eight to ten two-week sprints, opening with a fixed-fee two-week Sprint 0 readiness pass.

Stage: pre-contract / design-partner negotiation.

Duration: multi-phase, ~4–5 months to the executive pilot in production, then rollout.

Reporting: CTO and CEO are in the room at key points; you work day to day with the AI Architect (1.0 FTE) and a Data Engineer (0.5 FTE), and directly with the client's executive team.

Full-time role.

This is the most client-facing seat on the pod after the founders.

## WHAT YOU'LL ACTUALLY DO (EXAMPLE TASKS)

- Run executive distillation sessions — one-to-one with the Chief of Staff, CIO, CFO and COO — and turn each into a context pack: goals, OKRs, KPIs, investment policy, reporting standards, operating processes written down as usable text, not slides.
- Elicit and validate the business semantics of the ontology with stakeholders: what a "commitment", "decision", "priority", "portfolio update" actually mean in this group, and where definitions conflict between entities.
- Specify the agent skills per executive — scope, inputs, outputs, tone, acceptance criteria, escalation and human-in-the-loop boundaries — and write the evals that decide whether a skill is good enough to ship.
- Design the weekly alignment ritual in Slack: OKR-coached check-ins, drift detection, and the board master-report that assembles itself from the check-ins.
- Interpret process-mining output into a decision-ready report per team: where effort actually goes, what to automate, what to reorganise, what to leave alone — each with an ROI estimate and a recommended sequence.
- Build quick prototypes (no-code / low-code / prompt-level) to test a skill with an executive before engineering builds it properly.
- Own adoption: sit with the executives, watch them use it, find why they don't, and feed that back into the backlog every sprint.
- Measure payback after each automation ships and re-prioritise the next wave against it.
- Keep the written trail — decision records, requirement docs, runbooks — so the client's own team can eventually build the rest without us.

## SKILLS

- Executive stakeholder management and workshop facilitation — can hold a room of C-level people and leave with something written down
- Process analysis and mapping: current-state documentation, process-as-is vs. process-as-written, workflow redesign
- Requirements engineering for AI systems: user stories, acceptance criteria, eval design rather than vague wish-lists
- ROI / business-case modelling and prioritisation under constraints
- OKR / goal-management fluency — enough to coach, not just record
- Hands-on with LLM tooling: prompting, no-code/low-code prototyping, agent builders, evaluating output quality critically
- Comfortable reading process-mining / usage data and reasoning about it quantitatively (SQL or spreadsheet-level analysis is enough)
- Exceptional written English — most of your output is prose someone else acts on

## KNOWLEDGE

- Financial services / private equity operating context: investment policy, portfolio reporting, board and committee process, family-office structures — a strong plus
- AI governance basics in regulated environments: what to document, what needs a human, what needs an audit trail
- GDPR fundamentals as they apply to employee-generated data (mail, chat, meeting recordings) — including the politics of capture-by-default
- Awareness of ontologies / knowledge graphs — you don't build them, but you must be able to argue about definitions with the architect

## TRAITS

- Strategic thinker who can also do the unglamorous documentation work
- Comfortable telling an executive their stated process isn't the one the data shows
- Technically curious and genuinely hands-on with AI tools, without pretending to be an engineer
- Bias to writing things down; allergic to unresolved ambiguity

## EXPERIENCE

- 4+ years in business analysis, management consulting, process improvement or AI/product analysis
- Demonstrated experience eliciting requirements from senior stakeholders and shipping against them
- Hands-on LLM / generative-AI implementation experience — prototypes you can show, not courses you attended
- Experience mapping and redesigning real business processes, ideally with mining or usage data rather than interviews alone
- Background in or with financial services / investment firms — strong plus
- Comfortable as the sole analyst on a small (2.5-FTE) delivery pod

## About Neurons Lab

## Company Overview
- **One-liner**: Neurons Lab is an AI engineering partner that helps Financial Services organizations adopt AI and build production-grade agentic systems, from initial use case definition through deployment and continuous delivery.
- **Entity Type**: Private (Bootstrapped / Self-funded boutique firm)
- **Headquarters**: London, United Kingdom (International House, 64 Nile Str, London, N1 7SR)
- **Founded**: 2019
- **Founders**: Igor Sydorenko (CEO & Co-Founder) and Alex Honchar (CTO & Co-Founder)

## Core Business
- **Primary industry/industries**: AI Engineering & Consulting, focused exclusively on Financial Services (banking, capital markets, wealth management, insurance)
- **Target customers**: B2B — Fortune 500 financial institutions, large banks, wealth management firms, insurance companies, and adjacent regulated industries
- **Mission or purpose statement**: To help Financial Services organizations move from AI-curious to AI-enabled by building capability and deploying systems across core workflows — translating AI ambition into secure, scalable solutions that deliver commercial value.

## Products & Services
- **Custom AI Agents**: Deploy custom AI agents from discovery through production, enabling automation across core workflows with full auditability, traceability, and governance controls built in from the start. Decision logic is aligned with business rules and policies, designed for high-value workflows where accuracy, compliance, and scale are critical.
- **AI Training & Enablement**: Build AI fluency across teams, from engineering to the C-suite, through hands-on training grounded in the client's core workflows. Separate tracks for business and technical teams, with governance, compliance, and risk embedded from the start.
- **Continuous AI Delivery**: Post-deployment support with Forward-Deployed Engineers working alongside client teams to ensure systems continue to evolve. Governance, monitoring, and performance tracking are built in from the start, with AI adoption compounding across workflows over time.

## Market Standing
- **Valuation/Market Cap**: Not publicly available (private, self-funded boutique firm)
- **Key Metric**: Total Funding — Not disclosed (bootstrapped); the team reports 100+ AI implementations since 2019, including with Fortune 500 firms, and a team of 50+ AI engineers, architects, and analysts across Europe
- **Notable Investors/Partners**: Anthropic (partner), AWS (Advanced Partner, among first 15 to achieve AWS GenAI Competency in Agentic AI), Google Cloud (Partner)
- **Growth Signals**: Achieved AWS AI Competency in the Agentic AI category; named clients include HSBC (aligned 50+ senior leaders on AI deployment and governance) and Visa (scaled marketing operations across 9+ markets with LLM-based content system); works with leading Asian banks on agentic AI assistants for relationship managers

## Competitive Advantages
- **FSI Domain Expertise**: Deep specialization in Financial Services (banking, capital markets, wealth management, insurance) means no translation layer between business intent and technical execution. Compliance, governance, and risk are built into system design from the start.
- **Embedded Co-Creation**: Unlike traditional consultancies, they co-create with client teams through embedded delivery, working alongside internal stakeholders to transfer knowledge and expand impact from inside the organization.
- **Accelerated Delivery**: Pre-built FSI agent components and proven deployment patterns allow delivery of working, production-grade systems in weeks rather than months.
- **Boutique Senior Teams**: Small, senior teams (50+ engineers/architects) rather than large junior-heavy teams, enabling high-quality, fast execution.

## Strategic Focus
- Expanding agentic AI adoption within regulated financial environments, focusing on production-grade systems that operate within existing governance frameworks
- Building on partnerships with Anthropic, AWS, and Google Cloud to deliver cutting-edge AI solutions
- Deepening presence in wealth management, retail/corporate banking, private banking, SME banking, and insurance segments
- Scaling through a two-tier talent model (Core Team + Talent Network) to remain agile while growing

## Why Work Here
- **Remote-first culture**: Work from anywhere, with flexible hours — "design your day" philosophy
- **Benefits**: Unlimited PTO, flexible schedule, work on projects incorporating the latest AI technology (Anthropic, AWS, GCP)
- **Culture**: 7 guiding principles including ownership, genuine connections, and leadership development. Small, senior teams with high autonomy and low bureaucracy
- **Growth**: Opportunity to work on cutting-edge agentic AI for Fortune 500 financial clients, with clear paths to leadership roles
- **Team structure**: Two collaboration options — Core Team (full-time, benefits-eligible) or Talent Network (project-based, minimum 0.33 FTE)
- **Interview process**: Fast, streamlined — application → screening interview → validation interview → qualification interview → reference check

## Sources
1. [neurons-lab.com](https://neurons-lab.com/about/)
2. [neurons-lab.com](https://neurons-lab.com/)
3. [neurons-lab.com](https://neurons-lab.com/careers/)
4. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/neurons-lab.com)
5. [neurons-lab.com](https://neurons-lab.com/careers/core-team/)

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