Neurons Lab - AI Analyst (UA/RU Language speaking)

• 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. • Phase 3 (Distill) • a written optimisation report per team • 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. • Capture → Connect → Distill → Build • eight to ten two-week sprints • Stage: pre-contract / design-partner negotiation. • Stage • Duration: multi-phase, ~4–5 months to the executive pilot in production, then rollout. • Duration • 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. • Reporting • Full-time role. • Full-time role. • This is the most client-facing seat on the pod after the founders. • 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. • weekly alignment ritual • 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. • Interpret process-mining output • Build quick prototypes (no-code / low-code / prompt-level) to test a skill with an executive before engineering builds it properly. • quick prototypes • Own adoption: sit with the executives, watch them use it, find why they don't, and feed that back into the backlog every sprint. • adoption • Measure payback after each automation ships and re-prioritise the next wave against it. • Measure payback • Keep the written trail — decision records, requirement docs, runbooks — so the client's own team can eventually build the rest without us. • Executive stakeholder management and workshop facilitation — can hold a room of C-level people and leave with something written down • Executive stakeholder management • Process analysis and mapping: current-state documentation, process-as-is vs. process-as-written, workflow redesign • Process analysis and mapping • Requirements engineering for AI systems: user stories, acceptance criteria, eval design rather than vague wish-lists • ROI / business-case modelling and prioritisation under constraints • ROI / business-case modelling • OKR / goal-management fluency — enough to coach, not just record • OKR / goal-management fluency • Hands-on with LLM tooling: prompting, no-code/low-code prototyping, agent builders, evaluating output quality critically • LLM tooling • Comfortable reading process-mining / usage data and reasoning about it quantitatively (SQL or spreadsheet-level analysis is enough) • process-mining / usage data • Exceptional written English — most of your output is prose someone else acts on • written English • Knowledge • Financial services / private equity operating context: investment policy, portfolio reporting, board and committee process, family-office structures — a strong plus • Financial services / private equity operating context • AI governance basics in regulated environments: what to document, what needs a human, what needs an audit trail • AI governance • 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 • ontologies / knowledge graphs • 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 • 4+ years in business analysis, management consulting, process improvement or AI/product analysis • 4+ years • Demonstrated experience eliciting requirements from senior stakeholders and shipping against them • eliciting requirements from senior stakeholders • Hands-on LLM / generative-AI implementation experience — prototypes you can show, not courses you attended • Hands-on LLM / generative-AI implementation • Experience mapping and redesigning real business processes, ideally with mining or usage data rather than interviews alone • real business processes • Background in or with financial services / investment firms — strong plus • financial services / investment firms • Comfortable as the sole analyst on a small (2.5-FTE) delivery pod