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⚡ Source: ReedRef: 56868250

Lead Product Manager

Harnham - Data & Analytics Recruitment·London·Posted 3 weeks ago
💰 £90-110k/year
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Job description

Original text imported from Reed


Lead Product Manager


London. Up to 110000 base plus bonus and benefits.

This is a rare opportunity to take ownership of AI and machine learning product strategy within a high-growth consumer fintech. You will lead the discovery, scoping and implementation of intelligence-led experiences across a platform used by more than a million customers, with full autonomy to shape how AI enhances journeys, recommendations and personalised guidance.

The Company
They are a mission-led consumer finance business with a rapidly scaling mobile app and a strong reputation for customer-centric product design. After significant growth, they are now investing heavily in AI and ML for the first time and are building out new capabilities to deliver more personalised, context-aware experiences. You will join a collaborative product organisation based in London, working closely with engineering, data and decisioning teams.

The Role
You will:
* Define where and how AI and ML can be applied across the customer journey to drive personalisation and intelligent guidance.
* Lead end-to-end product management, from problem identification through to delivery and measurement.
* Provide strategic direction to teams with limited AI experience and champion best practices.
* Partner with engineering, design, data and decisioning teams to scope opportunities and define requirements.
* Drive systems-level thinking across journeys, ensuring intelligence is embedded consistently and effectively.
* Influence senior stakeholders and shape the long-term AI product roadmap.

Your Skills and Experience
You will bring:
* Strong experience delivering AI or ML-enabled features in consumer mobile apps.
* A background in end-to-end product management with a focus on personalisation, recommendations or intelligent automation.
* The ability to think in systems and design experiences that span multiple journeys.
* Strong collaboration skills and confidence working with data, engineering and leadership teams.
* An individual contributor mindset with high influence and strategic ownership.

What They Offer
* Salary up to 110000 plus a 15 percent bonus and a comprehensive benefits package.
* Hybrid working with two days a week in their London office.
* High strategic impact as the first product hire dedicated to AI and ML.
* The chance to shape greenfield AI capability across a major consumer platform.

How to Apply
If you are a consumer-facing Product Manager with experience delivering AI or ML features and want to take ownership of a transformative roadmap, apply today.


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Key skills

AI-extracted from the job advert

Must-have skills
AI product managementML-enabled features deliveryConsumer mobile app experienceEnd-to-end product managementPersonalisation systemsCross-functional collaboration
Nice-to-have
Fintech domain knowledgeRecommendation enginesSystems designStrategic stakeholder management
Soft skills
Strategic thinkingLeadershipCollaborationCommunicationAutonomyInfluenceProblem-solving
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Application advice

5 AI-generated recommendations to maximise your chances.

1

⭐ Highlight your AI/ML product management experience prominently as this is their first dedicated AI hire

2

📊 Quantify your impact: 'Led personalisation features increasing user engagement by 35% across 500k+ users'

3

🌐 Emphasise consumer mobile app experience as they specifically need someone with consumer-facing product background

4

🎯 Showcase systems thinking abilities as they want intelligence embedded consistently across multiple customer journeys

5

🤝 Demonstrate cross-functional collaboration skills with engineering, data and design teams as this role requires heavy partnership working

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Suggested CV bullets

3 bullets our AI drafted for this specific advert, mirroring its ATS keywords.

How to tailor your CV

Add these 3 bullets under your most recent experience:

  • Led end-to-end delivery of ML-powered personalisation features across consumer mobile app, increasing user engagement by 28% among 750,000 active users
  • Defined AI product strategy for recommendation systems, collaborating with 12-person engineering team to deliver intelligent guidance features within 6-month roadmap
  • Drove systems-level implementation of machine learning across 5 customer journey touchpoints, resulting in 22% improvement in conversion rates and £1.2M additional revenue

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Letter preview — tailored to Harnham - Data & Analytics Recruitment

Dear Hiring Manager,

Your Lead Product Manager role represents exactly the type of transformative AI opportunity I've been seeking. With proven experience delivering ML-enabled personalisation features and end-to-end product management in consumer mobile apps, I'm excited to shape your first dedicated AI product strategy.

My background in consumer-facing product management has equipped me with the systems thinking and cross-functional collaboration skills essential for embedding intelligence across customer journeys. I've successfully led discovery and implementation of recommendation systems that enhanced user engagement while working closely with engineering and data teams.

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Interview questions

10 questions generated from this advert.

Technical

  • How would you approach identifying the best opportunities for AI implementation across a consumer finance customer journey?
  • What frameworks do you use to evaluate the success of personalisation features in mobile apps?
  • How do you balance technical feasibility with user experience when scoping ML-enabled features?
  • What considerations would you make when designing recommendation systems for financial products?
  • How would you work with engineering teams to define requirements for AI features when they have limited AI experience?

Behavioural

  • Tell me about a time you had to influence senior stakeholders on a product strategy without direct authority
  • Describe a situation where you had to lead product discovery for a completely new capability
  • Give an example of how you've collaborated with cross-functional teams to deliver a complex product feature
  • Tell me about a time you had to think systemically about user experience across multiple touchpoints
  • Describe a situation where you took ownership of a transformative product initiative from conception to delivery
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STAR answer examples

Model answers using the Situation-Task-Action-Result framework. Adapt to your own experience.

1Question

Tell me about a time you had to influence senior stakeholders on a product strategy without direct authority

When leading the personalisation roadmap at my previous fintech company, I needed to convince the C-suite to invest £400,000 in ML infrastructure despite initial scepticism. I prepared a comprehensive business case showing how personalised recommendations could increase user retention by 18% based on competitor analysis and user research with 200 customers. I presented three implementation phases with clear ROI projections, demonstrating that the investment would generate £1.8M additional revenue within 12 months. By focusing on data-driven insights and addressing their concerns about technical complexity, I secured full approval and budget allocation. The initiative ultimately exceeded projections, delivering 23% retention improvement and establishing me as the go-to person for AI product strategy.
2Question

Describe a situation where you had to lead product discovery for a completely new capability

I was tasked with exploring AI-powered financial guidance features for our mobile app with no existing ML capabilities in the company. I started by conducting 45 user interviews to understand pain points around financial decision-making, then analysed usage data from 100,000 customers to identify patterns. Working with our data science team, I mapped out 8 potential use cases and created prototypes for the 3 most promising features. I facilitated workshops with engineering, design and compliance teams to assess technical feasibility and regulatory requirements. After 10 weeks of discovery, I presented a prioritised roadmap with clear success metrics and resource requirements. This groundwork enabled us to launch our first intelligent recommendations feature 6 months later, achieving 31% user adoption within the first quarter.

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