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I work with healthcare and life science companies as their Fractional Chief AI Officer: bridging boardroom strategy and hands-on implementation. 

No experiments. 
No buzzwords. 
Measurable results within weeks.
 

What I do:

• Identify where AI saves the most time and money in your specific business
• Build a prioritized AI roadmap with clear ROI
• Leading implementation – working with your team and trusted specialists to ensure it gets done
• Enable your people to work independently with AI
• Provide ongoing strategic sparring at C-level
 

Who I work with:

• PE portfolio companies scaling operations without adding headcount
• MedTech and pharma companies building AI capabilities
• Leadership teams who want strategy AND execution – not just slides
 
Past work: AI transformation programme for European pharma leadership. AI strategy workshops for financial services. Sales team AI enablement across life sciences.
 
Based in Munich. Working remotely across DACH and Europe.
 
Open to conversations about Fractional Chief AI Officer engagements, board advisory, and speaking.
 
→ karolynehahn.com

📩 [mail@karolynehahn.com] | 🌍 | 📞 [https://calendly.com/karolyne-hahn/20-min-zoom]

Karolyne Hahn

7 Free AI Courses for Healthcare Professionals.

Share this before your team spends money on expensive training:

1 – AI in Healthcare (Stanford University)
The gold standard. Free to audit on Coursera.
https://lnkd.in/diKZB5-a


2 – Generative AI for Healthcare (Google Cloud)
Specifically built for healthcare professionals.
Free to audit. Practical, not theoretical.
https://lnkd.in/d3twgWtQ


3 – AI for Medical Diagnosis (DeepLearning.AI)
For those who want to go deeper. Image analysis, diagnostics.
https://lnkd.in/dCQAS4K7


4 – How to Use ChatGPT in Healthcare (edX)
The most practical one. Day-to-day applications.
edx.org → search “ChatGPT in Healthcare”


5 – Elements of AI (University of Helsinki)
Best starting point for non-technical leaders.
Free. Certificate included.
elementsofai.com


6 – AI for Everyone (Andrew Ng / Coursera)
Not healthcare-specific but the best foundation.
Free to audit. Mandatory for any healthcare leader.
https://lnkd.in/dFhuxXZQ

7 – Fundamentals of Machine Learning for Healthcare (Stanford)
For those ready to go beyond ChatGPT.
https://lnkd.in/dtz8sWBM

Save this. 
Share with your team. 
Forward to your CEO.


#HealthcareAI #FreeLearning #MedTech #PharmaAI #AIEducation

10 AI tools healthcare teams can use for free right now. No hype. Only what actually works.

→ 1. Nabla Copilot - AI clinical documentation. 30 free consultations/month. nabla.com

→ 2. Glass.health - Differential diagnosis + ambient scribing. Free trial. glass.health

→ 3. DeepScribe - Voice-to-clinical-notes. Free trial available. deepscribe.ai

→ 4. TL;DV - Meeting documentation + transcription. Free tier. tldv.io 

→ 5. Perplexity AI - Medical literature research with citations. Free. perplexity.ai

→ 6. Claude.ai - Summarize regulatory documents, policies, reports. Free tier. claude.ai

→ 7. Elicit.org - Accelerate systematic reviews. Free for researchers.
elicit.com

→ 8. Consensus.app - Evidence-based answers from 220M+ papers. Free version.
consensus.app

→ 9. Synthesia - Create internal AI training videos. Free trial. synthesia.io

→ 10. Notion AI - Structure clinical SOPs and protocols. Integrated. notion.so/product/ai

Which of these have you already tested?
Save this and share with your team.

Who on your board owns this?
 
Share and save for your next board meeting.

Fractional Chief AI Officer | Healthcare & MedTech | Board Advisory | Speaker

If you’re reading this, you’re statistically in the top 0.04% of AI adoption on Earth.

You‘re not late. 
And no, AI is not a bubble. 
It might be your timeline tricking you.
Most people don’t know what Claude is.

Ps. The red dot at the bottom right corner are people that vibe code apps and tools from scratch (eg. using tools like Lovable, mostly without even knowing how to code).

Save and share it

Cc Ben Tannenbaum

I've been quiet on here for a while.
 
Honest reason: life happened. A difficult personal year that took most of my energy.
 
But I've used that time to get very clear on what I do – and who I do it for.
 
I work with healthcare and life science companies as their Fractional Chief AI Officer.
 
Not as a consultant who delivers a deck and disappears.

Not as a trainer who runs a workshop and moves on.
 
As someone who stays until AI actually works inside the organization.
 
I've seen too many companies go through the motions – workshops, strategies, pilots –
only to watch everything dissolve back into daily business three months later.
 
That's the problem I solve.
 
If you're leading a healthcare or MedTech company and AI hasn't stuck yet – I'd love to talk.
 
#FractionalCAIO #HealthcareAI #MedTech #AIStrategy #LifeSciences

I've worked with leadership teams across pharma and life sciences.
The pattern is always the same.
 
Workshops go well. Energy is high.
People see what AI can do for their specific pain points. 
(And there's always a solution – that part never fails to surprise them.)
 
Then the session ends.
 
Without top management commitment – a named owner, a protected budget, a clear mandate – everything quietly returns to business as usual within weeks.
 
AI doesn't fail because of the technology.
It fails because of organizational gravity.
 
The companies that get results are the ones where someone at the top has personally decided: 

This Is Happening.
 
Everything else is just noise.
 
Is your leadership team aligned on AI – or just interested?

 
#HealthcareAI #AILeadership #MedTech #FractionalCAIO #LifeSciences

No alternative text description for this image

The gap between 'having an AI strategy' and 'AI actually working' is the most expensive gap in healthcare operations right now.

An AI strategy that lives in a document 
is not a strategy.

It's a liability because it creates 
the illusion of progress.


Real AI strategy looks like this:

→ One named person responsible
→ One process changed per quarter
→ One metric to measure it
→ One honest review every 30 days

That's it. No fancy roadmap deck needed.


#HealthcareCEO #AIStrategy #MedTech #HealthcareAI #Leadership

The EU AI Act is officially in force.
Healthcare boards are panicking.
You shouldn't.

I read all 400+ pages so you don't have to.
Here are the 8 things every healthcare leader needs to know now:

1. High-Risk is the Default
Almost all clinical AI systems qualify.
This isn’t a "maybe" or a "borderline" case.
If it touches a patient, it's likely High-Risk.

2. Conformity Before Deployment
Assessment is mandatory before you go live.
Not after. No "grandfathering" in old systems.
Compliance starts at the design stage.

3. Human Oversight ≠ "A Doctor Looks at It"
"A human is in the loop" is a phrase, not a strategy.
You need documented protocols on how humans intervene.
If it’s not in writing, it didn’t happen.

4. Radical Transparency
You must disclose AI use to patients.
Trust is the new currency.
Transparency is the only way to earn it.

5. Validation is Never "Done"
One-time validation reports are dead.
Ongoing performance monitoring is the new standard.
AI evolves; your oversight must evolve faster.

6. Data Provenance is King
Where did the training data come from?
Is it representative? Did you test for bias?
Show your work. All of it.

7. The Clock is Ticking
- New systems: The time is now.
- Existing MDR/IVDR systems: You have until 2027–2029.
The runway is shorter than you think.

8. Compliance as a Competitive Edge
Don't view this as a hurdle. View it as a moat.
Organizations building for compliance now will lead the market in 2028.
Chaos is a ladder—if you have the right map.

The Hard Truth:
Most boards are waiting for a "compliance officer" to handle this.
That is a €35M mistake. (Or 7% of global turnover).

The EU AI Act isn't a legal checkbox.
It’s a fundamental shift in how healthcare tech survives.

The Question:
Who on your board actually owns the AI Risk Map?
If the answer is "no one," you're already behind.

💾 Save and share this post. You’ll need it when the 2027 deadline hits.

I've watched 4 pharma AI pilots fail in 2 years.

Same reason every time.

They got the anatomy wrong.

Here's what a pilot that actually scales looks like:

→ Start with pain, not technology 
"We lose X hours/week because of Y." 
No specific pain = no real use case.

→ Audit your data before you build anything 
Is your CRM 80%+ complete? 
Are HCP records deduplicated? 
If no: fix that first. AI won't save bad data.

→ Pick boring over ambitious 
Not the sexiest use case. 
The one your reps will actually use on Monday.

→ 4 people make or break the pilot 
1 champion. 1 data owner. 1 frontline user. 1 exec sponsor. 
Missing one = it dies quietly.

→ Define "working" on day 1 
Not "improve efficiency." 
But: "Pre-call prep under 15 min within 8 weeks." 
3 metrics. Measurable. Non-negotiable.

→ Week 1 is everything 
Let one rep generate one AI brief. 
Watch their face. 
That reaction is your internal case study.

→ 90 days. Then decide. 
2 of 3 metrics hit? Scale. 
If not? Diagnose. Re-pilot. 
Never just abandon.

The transformation doesn't start with the technology.

It starts with getting the anatomy right.

♻️ Save and repost if this is useful for someone building AI initiatives in pharma or medtech.

The Anatomy of a Pharma AI Pilot

Viele Vorstände behandeln AI wie ein Software-Update. Nett zu haben, aber delegierbar.

Falsch. 
AI entwickelt sich rasant zu einem reinen Board-Thema. 

Nicht wegen der Technik, sondern wegen der existenziellen Einschläge:

1. Angreifer brauchen keine 10 Jahre mehr, um den Markt abzunehmen. Sie brauchen nur ein besseres Modell und weniger Overhead.

2. Die aktuelle Wertschöpfung wird in Echtzeit entwertet. Was heute Marge bringt, ist morgen Freeware.

3. Wer "AI" nicht versteht, kann sie nicht führen. Wer sie nicht führt, haftet für die Konsequenzen.

Wer wartet, bis die IT-Abteilung antreibt und eine Lösung präsentiert, hat verloren.

Agree?

Two sentences are killing European healthcare.

They don’t sound like failures.
They sound like "careful leadership."
1. "That’s an IT decision."
2. "Global has to decide."

Both mean exactly the same thing:
„Not my problem.“


1. The Ownership Trap

When a CEO says "That’s an IT decision," they aren't being humble.

They are outsourcing the soul of the company.
• IT manages infrastructure.
• IT reduces risk.
• IT maintains systems.

IT was never designed to answer:
• What does AI mean for our workforce?
• Where is human judgment irreplaceable?
• What kind of company do we want to be in 2030?

Those aren't technical hurdles.
Those are leadership mandates.
The moment you hand them to IT, 
the vision dies in a support ticket.


2. The "Global" Mirage

I’ve seen organizations wait years 
for "Global" to approve a framework.
While you wait, your competitors move.

Understand this:
- Silence is a decision.
- Waiting is a decision.
- "Let’s see how it develops" is a decision.

You aren't being "strategic."
You are delegating ownership to nobody.


The Reality Check

AI isn't a tech stack. 
It's a leadership choice.

If you treat your future like a software update, don't be surprised when your organization crashes.

P.S. If your CEO’s first reaction to AI is "Ask IT," you’re not leading a company.
You're managing a museum.

Agree?

Die meisten Pharma-Vertriebler hassen ihr CRM. Sie pflegen Daten ein, um Ruhe vorm Chef zu haben, nicht um besser zu verkaufen. 

Das Ergebnis? Millionen Euro für Lizenzen (Veeva, Salesforce), aber null Impact auf die Verschreibungsquote.

Warum? Weil die Angst vor der DSGVO jede intelligente Nutzung blockiert.


Hier sind drei konkrete Hebel, wie AI das CRM rechtssicher zum Profit-Center macht:

Beispiel 1: Der "E-Mail-Clustering" Bot (Datenschutzkonform): 

Statt dass ein Mitarbeiter mühsam liest, welche Betreffzeile bei welchem Onkologen zieht, anonymisiert eine AI die Interaktionsdaten. 

Sie erkennt: „Ärzte in Unikliniken öffnen Studien-Links montags um 8 Uhr, niedergelassene Ärzte bevorzugen Video-Snippets am Mittwochabend.“

Der Witz: Die AI lernt das Muster, nicht den Namen. Der Datenschutz bleibt gewahrt, die Klickrate verdoppelt sich.


Beispiel 2: Voice-to-CRM (Effizienz): 

Der Außendienst verliert 20 % seiner Zeit mit Tippen. 

Die Lösung: Ein lokales, verschlüsseltes Sprachmodell. Der Mitarbeiter spricht das Besuchsresümee ins Handy. Die AI filtert medizinische Insights heraus, kategorisiert sie nach Compliance-Vorgaben (keine unzulässigen Heilversprechen!) und füttert das CRM.

Der Clou: Die AI fungiert als „Compliance-Filter“, bevor der Text überhaupt dauerhaft gespeichert wird.


Beispiel 3: Churn-Prediction für Apotheken: 

Die KI erkennt im CRM: „Apotheke X hat seit 3 Monaten keine Schulung mehr angefragt und die Bestellintervalle vergrößern sich.“

Die Aktion: Das CRM schickt nicht blind den Außendienst vorbei, sondern triggert automatisiert ein individualisiertes Fortbildungsangebot.

Die Realität: Datenschutz ist kein technisches Hindernis, sondern eine Frage der Architektur. Wer AI in das CRM integriert, statt sie davor auszusperren, macht aus einer Protokoll-Pflicht eine Wachstums-Maschine.

Hören wir auf, Daten zu sammeln. Fangen wir an, sie zu nutzen.

Agree?

A pharma leadership team.
3 half-day workshops.
One honest lesson.

I worked with a European pharma company's leadership team to identify where AI could solve their biggest operational pain points.

We went deep. Mapped processes. Prioritized opportunities.

The energy in the room was real.
By the end, they knew exactly which 5 areas AI could transform - and in what order to tackle them.

Then: the Managing Director left. 
Internal restructuring. 
Global compliance questions.
The roadmap sat in a folder.

The lesson wasn't that AI failed. The lesson was that strategy without implementation ownership is just an expensive workshop.

I don't deliver roadmaps. 
I stay until the change is real.
That's a different kind of engagement. 
And it's the only kind that works.

DM me if you're ready for that.

#PharmaAI #AIImplementation #HealthcareAI #ChangeManagement #FractionalCAIO

6 signs your AI initiative will fail by 2027.
Most companies aren't "innovating."
They are stalling.

If these 6 signs look familiar, you aren’t in an "evaluation phase."
You’re in a graveyard.


1. Zero Accountability

There is a team. A committee. A working group.
There is no single name.
If nobody’s career depends on it, nobody is working on it.


2. Vague Definitions

Bad: "Improve patient outcomes."
Good: "Reduce ICU documentation by 40% by Q3."

If success isn't one sentence with a number and a date, it’s a hobby.


3. Governance Theater

The last meeting produced a slide deck, not a decision.
Real governance says "No" more than it says "Maybe."
Everything else is just a coffee break.


4. Activity ≠ Outcomes

Your board sees reports on:
• Pilots completed.
• Workshops held.
• Vendors met.

They should be seeing:
• Cost per case.
• Hours saved.
• Errors caught.

Stop reporting on how busy you are. Start reporting on how much you saved.


5. The Ghost Sponsor

The project sponsor changed roles 8 months ago.
The initiative is still "running."
But nobody knows who is driving the bus.


6. The Regulation Excuse

"We’re waiting for the EU AI Act to settle."
Winners aren't waiting for permission.
They are building flexible governance that complies by design.


2027 is closer than you think.

In two years, you will either be a Case Study or a Cautionary Tale.

P.S. What’s the biggest bottleneck in your industry? 👇

There is no middle ground.

Last Friday, Donald Trump didn't just criticize Anthropic. He blacklisted them.

Effectively immediately: 
A "National Security Supply Chain Risk."

The Crime?
Anthropic refused to let the Pentagon use Claude for "unrestricted lethal operations."

They stood by their safety guardrails.
Washington responded with a total ban and threats of "criminal consequences."

If you are in Pharma, MedTech, or Healthcare, the "Safe AI" era is over.

You are caught in the crossfire of a Geopolitical War.


The 3 Brutal Realities for 2026:

1. Compliance is a Liability: If you have US federal contracts, your "safe" Claude-R&D stack is now a legal ticking time bomb.

2. OpenAI is the "State Model": Speed over safety. Military access over ethical guardrails. That is the new US mandate.

3. The Sovereign Pivot is Mandatory: If you don't own the weights and host them on your own servers, you don't own your research.

The Move:
Stop buying subscriptions.
Start building a fortress.
Own the model. Control the hardware.

The "Old World" follows the landlord.
The "New World" owns the house.

Where is your intellectual property tonight?

#PharmaAI #AnthropicBan #DigitalSovereignty #AIStrategy #TrumpAI 

Image cc evolving ai

The 6-Week AI Pilot Plan For Pharma Sales (Week By Week)
You want to run an AI pilot in your sales team.
Here's the exact 6-week plan:

Week 1: Diagnose before you build
Don't touch technology yet.
Interview 10 reps. 

Ask:
→ "Where do you lose the most time every week?"
→ "What information do you wish you had before every HCP call?"
→ "What does a great prep session look like vs. a bad one?"
You will hear the same 3 answers from almost everyone.

Those 3 answers are your use cases.

Week 2: Fix your data
Choose one use case from Week 1. 
Audit the data you need for it.

CRM completeness: are call notes filled in? 
Are HCPs deduplicated? 
Are territories current?

You cannot build good AI on bad data.
Don't skip this week. 
It's the most important one.

Week 3: Build the simplest possible version
MVP only. 
No integrations. 
No fancy dashboards.

If the use case is pre-call briefs:
→ Build a simple template
→ Have a team member manually compile it for 5 HCPs using existing data
→ Give it to 3 reps and watch them use it

If the brief helps them, you've proven the concept.
Now you automate.

Week 4: Automate and test
Connect your data sources. 
Generate the brief automatically.
Test it on 20 HCPs. 

Check accuracy. 
Fix errors.
Let reps give real-time feedback — what's missing, what's irrelevant.

Week 5: Measure obsessively
Three metrics only:
→ Time saved per rep per week (self-reported + CRM logs)
→ HCP meeting quality (rep-rated on 1–5 scale pre/post)
→ Call conversion rate (meeting request → accepted)
No other metrics matter at this stage.

Week 6: Decision and scale
If metrics improved: write up the results. 
Present to leadership. 
Get budget for Phase 2.

If metrics didn't improve: 
Go back to Week 1 with what you learned. 
Change the use case or the approach.
Don't kill the initiative because one use case didn't land.
Learn and iterate.

6 weeks. One use case. Real data.
That's how AI transformation actually starts.

Save and share 🙋🏻‍♀️

PE investors are sitting on an untapped value lever in their portfolio companies.

It's not a new product. 
Not a new market.
It's operational efficiency through AI.

Here's the math.

A healthcare company with 80 employees,
each losing 1.5 hours/day to tasks AI could handle:

→ 120 hours/day recovered
→ ~600 hours/week
→ Equivalent to 15 additional FTEs

→ At €60k average cost = €900k/year in recovered capacity
Without hiring a single person.

Most PE-backed healthcare companies haven't touched this yet.
Not because it's hard. 
Because nobody has made it a priority.

That's what a Fractional Chief AI Officer does for your portfolio.

One engagement. 
Measurable EBITDA impact. 
Scalable across companies.

Curious what this could look like for your portfolio? 
Let's talk.

#PrivateEquity #HealthcareAI #PortfolioValue #MedTech #FractionalCAIO

Die meisten Unternehmen scheitern nicht an AI, sondern an Führungsentscheidungen rund um AI.

Nicht, weil die Technologie unklar wäre, sondern weil sie drei unbequeme Punkte aufwirft:

1. AI zwingt zu strategischer Priorisierung.
Man kann nicht mehr „n bisschen experimentieren“.

2. AI verschiebt Machtstrukturen.
Daten, Entscheidungsbefugnisse und Verantwortlichkeiten verändern sich.

3. AI macht fehlende Leadership-Alignment sichtbar. Viele Führungsteams sind sich nicht wirklich einig, was strategisch zählt.


Deshalb ist AI kein IT-Thema, sondern ein Führungsthema.

Und gehört ins Board-Level.


Die entscheidende Frage ist nicht mehr:
„Sollen wir AI einsetzen?“

Sondern.. 

„Wo schafft AI echten Wettbewerbsvorteil - und wo nicht?“

Agree?

I used to tell clients AI would transform their medical affairs team.
I was wrong. 
AI doesn't transform medical affairs.
It gives back the 90 minutes before the real work starts.

The real work - medical judgment, HCP relationships, regulatory strategy - AI can't touch that.

What AI can do: structure a clinical study summary so your team spends 10 minutes reviewing instead of 90 minutes writing.
That's it. That's the honest promise.

Here's the prompt that delivers it. 

"You are a Senior Medical Affairs Manager preparing a briefing for a specialist physician. The physician has 3 minutes and is deciding whether this data changes their prescribing behaviour.

BEFORE YOU BEGIN: Identify and state: (1) study type - RCT, observational, meta-analysis, or single-arm (2) whether a comparator exists (3) study phase. Frame your entire summary through these three facts.

STUDY IN ONE LINE: Drug name, mechanism class, indication, population, phase, duration. One sentence. No interpretation.

HEADLINE NUMBER: Absolute risk reduction AND relative risk reduction. 
If only relative risk is reported - flag this explicitly. 
Relative-only reporting is a red flag.

SAFETY SIGNAL: Exact incidence rates. Discontinuation rate due to AEs. 
If no significant findings - state explicitly. Do not skip.

VS. STANDARD OF CARE: How does this compare to what the physician currently uses? If no head-to-head data exists - state explicitly. 
Do not imply comparison where none was made.

WHAT CHANGES IN PRACTICE: Would a physician rationally change a treatment decision based on this data alone? 
If yes - for which patient profile specifically? If no - say so.

STATISTICAL ROBUSTNESS: Sample size. P-value. Confidence interval. 
Flag if present: underpowered sample, wide confidence intervals, high dropout rate, per-protocol vs intention-to-treat discrepancy.

LIMITATIONS: Maximum 3. Prioritise what a regulator or payer would flag - not what the authors listed.

EXPERT REVIEW FLAGS: List every claim requiring medical-legal review before HCP use. Cite the claim - not just the section.

CONTRADICTIONS: If any data appears internally inconsistent - flag it. Do not smooth over contradictions.

Format: Bullet points only. No jargon. Max 260 words. Never speculate beyond the data. If insufficient data for any section: write exactly 'Insufficient data.' Do not assume."

Paste into your fav. AI, e.g. Claude, Copilot.
Upload any clinical study.

What took a medical writer 90 minutes now takes 3.

Save and share this prompt.

One honest warning:
AI still hallucinates in medical contexts. 

Especially:
→ Numbers from PDF tables: often extracted incorrectly 
→ Confidence intervals and p-values: sometimes invented 
→ Study population details: summarised instead of cited

#MedicalAffairs #PharmaAI #HealthcareAI

Anatomy of a Medical Affairs AI Prompt

Die eigentliche Herausforderung von AI ist nicht die Technologie.

Sie ist organisatorisch.

Denn AI verlangt von Unternehmen:

• schneller zu entscheiden
• stärker zu priorisieren
• transparenter zu führen
• mutiger Verantwortung zu übernehmen

Viele Organisationen sind darauf nicht vorbereitet.

Nicht, weil sie die Technologie nicht beherrschen.
Sondern weil ihre Entscheidungsstrukturen aus einer anderen Zeit stammen.

Die Unternehmen, die AI erfolgreich nutzen werden, sind nicht die mit der besten Technologie.

Sondern die mit starkem Leadership. 

Agree?

The most underused AI tool in healthcare companies right now:
Meeting intelligence.

Here's the setup that saves my clients hours every week:

1. Every internal meeting gets recorded (with consent, always)
2. AI tool (tldv , Jamie, or Microsoft Copilot ) auto-transcribes
3. After the meeting, AI generates:
  → Key decisions made
  → Action items with owners
  → Open questions
  → 3-sentence summary for stakeholders who weren't there

The meeting ends. 
The follow-up is already written.

In pharma and MedTech, where documentation is everything,
this alone saves a team lead 3–4 hours a week.

Setup time: 20 minutes.
ROI: immediate.

What's your current meeting documentation process?

#HealthcareAI #MeetingIntelligence #AITools #PharmaOps #MedTech

The most underused AI tool in healthcare companies

Michael Williams

Talent Acquisition Lead ✦ RL100 Member ✦ Views are my own

Over 19 years experience and have previously worked at HSBC, UBS and BNY Mellon. I am very passionate about my work and have had the privilege to partner with Global Banks and Financial Institutions in the identification and attraction to hire senior leaders, ranging from C-Suite to Board level across the UK, Europe, North America and APAC. 

Experience working at the intersection of Executive Recruitment, Talent Intelligence, Talent Management and Workforce Planning, I support HR Leaders in building high-performing teams that drive growth and transformation. 

Core Specialties:
➝ Executive Recruitment & Talent Acquisition (C-Suite, CEO -1 and -2 level, MD, Board).
➝ Talent Intelligence, Market Insights & Competitor Benchmarking.
➝ Workforce Planning, Succession Strategy & Leadership Assessment.
➝ Senior Stakeholder Management & People Leadership.
➝ Direct sourcing & Vendor Management.
➝ Successfully built a TA function from the ground up.
➝ Built C-Suite Leadership teams via direct sourcing channels and networks.

Industry Focus:
➝ Banking, Financial Services, Wholesale Banking, Asset Management, Investment Management, Private Bank, Investment Banking.
➝ Front Office, Middle Office, Corporate and Control Functions.

Chartered MCIPD qualified professional who has a data-driven and hands-on approach, combining analytical insight with strategic execution to deliver measurable impact in leadership hiring and talent strategy.

Head of Talent

Recruitment Lead responsible for all lines of business hiring from entry to Board level, covering Corporate Functions through to the FrontI office. 

2021 Achievements:
→ Recruitment Framework & Governance Implementation 
→ IR35 Lead (Successful HMRC Audit)
→ Reduced time to hire to 41 days

2022 Achievements:
→ ATS Implementation 
→ Talent Management Framework Design
→ Gender Diversity recruitment policy implementation 

2023 Achievements (YTD):
→ Direct recruitment model implementation 
→ Direct Recruitment (91% YTD)
→ Internal Mobility (63% increase YTD)
→ Branding

Key Placements:
→ European Chief Risk Officer, London
→ Chief Risk Officer, Paris
→ European Head of Internal Audit
→ UK MLRO / European Head of Financial Crime
→ European Chief Financial Officer
→ Head of Market Risk & Reporting
→ Head of Credit Risk
→ Head of Enterprise Risk Management 
→ Head of Operations 
→ Head of Business Management
→ European Head of Banking
→ European Head of IT
→ Head of Communications - Europe
→ European Head of Treasury & Financial Markets
→ Business Manager to the CEO, IB

Leadership, Front Office & Corporate Functions Recruitment | Banking & Financial Services

City Of London, England, United Kingdom (On-site)

Job by Natixis Corporate & Investment Banking

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