RFRamona Furter
See the model
Application · MultiBank Group · Dubai

I build the operating model that lets AI ship safely inside regulated finance.

Twenty-one years building products and businesses, today leading AI business models at Swiss Post, with much of my career inside regulated, money-moving organisations. This site is my application for Head of AI, Data & Governance. It includes an honest read on where I'm strong and where I'd lean on the team.

See how I'd build the function Read the honest fit first
21 yrsbuilding products & businesses
AI leadAI business models, Swiss Post
Regulated financeMobiliar · UBS · Baloise
Hands-onLLM · RAG · governance by design
Portrait of Ramona Furter
Before anything else

An honest read on the fit

This is a Director-level role, and the posting asks for 12+ years in data, analytics, AI or governance and 5+ in senior leadership of those teams. I won't pretend my CV maps onto that line for line. Here's the honest split: what I'd bring on day one, and what I'd be growing into with the team and my peers.

What I bring on day one

  • AI strategy that ships. I lead AI business models now, from sizing the opportunity to running the roadmap, and I'm hands-on with LLMs, RAG and agents, not just sponsoring them.
  • Regulated, money-moving context. Years inside insurers and bank partnerships where compliance is the constraint, not an afterthought.
  • Governance-by-design instinct. I build safety, auditability and human review into the flow rather than bolting them on. The operating model below shows how.
  • Executive fluency. I've reported to C-level, owned UBS and Baloise partnerships and raised two rounds. I can make the case upward and hold the line.
  • Building and growing teams. Founding team that grew to 23 people across 10 locations; led cross-functional squads and agencies to ship.

What I'd grow into

  • Enterprise data governance at scale. I've run data quality and KPI work on a CHF 100M+ business, not a group-wide stewardship council across 100 countries. That's a step up I'd take on deliberately.
  • Deep MLOps and model-risk management. I think in evaluation, drift and audit, but a regulated trading group needs formal model-risk practice I'd build with specialists.
  • Cloud data platforms in depth. I'm fluent in the concepts of Snowflake, Databricks and the major clouds; hands-on platform depth would come from the data engineers I'd hire and lead.
  • Director-level tenure. I've led teams and functions, not a 5-year run heading a data or AI organisation. I'd treat the first year as exactly that proving ground.
So why apply? Because this function is being stood up, and standing things up from ambiguity is the thread through my whole career. You can hire a 15-year data-governance veteran who has run the same playbook elsewhere, or someone who pairs real AI-in-regulated-finance instinct with the drive to build the function from first principles and the honesty to know what she'd hire around. If that trade interests you, the rest of this page makes the case.
Mapped to the posting

What the role asks, and where I've done a version of it

The posting splits into AI strategy, data strategy, governance and compliance, analytics, and leadership. Here's each, matched honestly to real work, with the proof underneath.

01

Group-wide AI strategy and adoption

I take an ambiguous AI opportunity, turn it into a real product with a cross-functional squad, and drive it into use. Today that's whole AI business models from concept to launch. Scaling it group-wide is the growth edge I'm open about.

Proof: AI Project Lead for business development at Swiss Post; built my own products end to end with AI workflows.

02

Data as a strategic asset

I've treated data as the thing that decides, not decoration: defining the metrics up front, running the experiments, and letting evidence settle the call. On a CHF 100M+ business that meant real money moved on the numbers.

Proof: +9% conversion and +15% checkout lift at ifolor through research, A/B testing and analytics; comfortable to SQL-level questions.

03

Governance, compliance and model risk

My instinct is governance-by-design: safety classifiers, grounding, audit trails and human review built into the path. I've worked where compliance is the gate, in insurance and bank partnerships. Formal model-risk practice I'd build with specialists.

Proof: owned UBS and Baloise partnerships at Brixel; ran innovation inside Die Mobiliar; the operating model below is mine, guardrails included.

04

Analytics, BI and data literacy

I've built the KPI frameworks teams actually steer by, and pushed analytics from a reporting back-office into a decision tool the whole team uses. Raising data literacy across a group is the same job at larger scale.

Proof: owned the e-commerce KPI framework and analytics at ifolor; ran hypothesis-and-data growth across several ventures at Sparrow Ventures.

05

Building and mentoring multidisciplinary teams

Getting engineers, designers, analysts and partners to one outcome is the part I'm best at. I've hired, built and led mixed teams from nothing, owning the budget, the KPIs and the hard prioritisation calls.

Proof: founding team that grew WePractice to 23 people across 10 locations; led cross-functional teams and agencies at ifolor.

06

Executive partnership and stakeholder management

I've sat across from C-level and investors, made the case, and held the line when it mattered. A group function lives or dies on that trust, and on translating between the board, the regulators and the engineers.

Proof: reported to C-level at ifolor; main bridge to senior client stakeholders at Brixel; raised two funding rounds.

Curriculum vitae

Ramona Furter

AI and product lead in Zurich with over twenty years of experience, much of it inside regulated finance and insurance, open to relocating to Dubai. I turn ambiguous problems into shipped products and measured results, increasingly with AI at the core. German and Swiss German native, English fluent, French conversational.

Jan 2026 to present

AI Project Lead, Business Development

Swiss Post, Advertising · Zurich

  • Lead AI-driven business models for Swiss Post Advertising, from sizing the opportunity to building and running the roadmap.
  • Turn AI ideas into go-to-market plans and new revenue, tracked with clear KPIs.
  • Run cross-functional work from concept to launch across product, tech, data and commercial teams.

Oct 2024 to Jul 2025

Senior Product Manager, Lead E-Commerce

Ifolor Group · Zurich

  • Owned the e-commerce ecosystem, KPI framework and strategy for a CHF 100M+ business, reporting to C-level.
  • Lifted conversion 9% and the checkout step rate 15% through research, A/B testing and analytics.
  • Led a cross-functional team and external agencies, owning budget, resourcing and KPIs.

Jun 2023 to Sep 2024

Lead Project Manager

Brixel · Zurich

  • Owned the partnerships with financial institutions, UBS and Baloise, that drove growth.
  • Was the main bridge between senior client stakeholders and the internal product team.

Mar 2020 to May 2023

Marketing & Growth Lead, Founding Team

WePractice · Sparrow Ventures (Migros Group) · Zurich

  • Founding team of a mental-health venture. Closed two funding rounds and grew it to 10 locations, 23 people and 170+ customers.
  • Generated 1000+ client matches in year one and built the full go-to-market on a hypothesis-and-data approach.
  • Built and led the marketing and sales team after Series B, owning budget, KPIs and growth.

Sep 2019 to Sep 2022

Growth & Venture Builder

Sparrow Ventures · Zurich

  • Built and ran growth and go-to-market for several internal startups, from early validation to scale-up.
  • Used research and experimentation to improve conversion, lower acquisition cost and raise customer lifetime value.

Jan 2017 to Aug 2019

Intrapreneur, Innovation

Die Mobiliar · Bern

  • Ran market pilots for new products (Smide, now BOND Mobility, plus XperCheck and Lizzy) from MVP to launch, inside one of Switzerland's largest insurers.
  • Coached cross-functional teams and explored new data and partnerships.
A worked operating model

How I'd stand up AI, Data & Governance

Not a slide of buzzwords. The four pillars the posting names, taken the way I'd actually run them: the strategy, how data becomes an asset, how governance sits in the path rather than on top of it, and what a sequenced first year looks like. Click through the four tabs.

Worked model · illustrative targets
Pillar 01

A group-wide AI strategy, prioritised by value and risk

Start from where AI moves a real number for a trading group, not from the model. Pick a small set of bets, each with an owner, a metric and a risk tier, so the board can see the portfolio at a glance.

Bet A · Lower risk
Internal productivity AI

Copilots for support, compliance review and ops, behind the firewall on the group's own data. Fast value, contained blast radius, a safe place to build the muscle.

Measured by: hours saved, ticket deflection, review turnaround.
Bet B · Medium risk
Client-facing intelligence

Grounded answers about a client's own portfolio and the markets, in plain language, fully audited and never crossing into regulated advice.

Measured by: engagement, CSAT, zero compliance incidents.
Bet C · Higher risk
Risk & surveillance models

ML for fraud, AML signals and trading surveillance, where model risk is real and governance has to lead. Built last, with the most rigour.

Measured by: detection rate, false-positive load, audit pass.
From day one

My first 90 days

Before scaling anything, I'd earn the ground truth and prove the governance backbone. Roughly how I'd spend the first three months as Head of AI, Data & Governance.

Phase 1 Days 1 to 30

Learn the estate, honestly

  • Meet the data, engineering, risk and compliance leads, and the executives this function serves.
  • Map where AI and data already live, where they stall, and the real regulatory constraints across the markets we operate in.
  • Take a frank inventory of data quality, model inventory and governance gaps. No spin, just the baseline.
Phase 2 Days 31 to 60

Stand up the backbone

  • Draft the governance framework, stewardship model and AI risk-tiering with the people who'll run it.
  • Pick the first contained AI win and the few data domains to make trustworthy first.
  • Align the board, risk and engineering on the sequence, the metrics and what "scale gated on audit" means in practice.
Phase 3 Days 61 to 90

Ship one thing, prove the model

  • Get a first internal AI win into production behind the guardrails, measured against agreed numbers.
  • Convene the governance council for its first real decision, not a launch slide.
  • Report progress honestly, wins and gaps, and turn it into the roadmap for the rest of year one.