Head of Product (AI)

Finatal
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Data Analyst - HOTH, Permanent

Data science programme lead

Senior Machine Learning Engineer

Manufacturing Data Scientist

Senior Data Engineer/ PowerBI

Job Title: Head of Product (AI)

Reports To: CEO/Chief Product Officer


DW005


Role Overview:

As the Head of Product (AI), you will be responsible for shaping and executing the AI product strategy to drive value across our business. You will lead cross-functional teams in product development, work closely with key stakeholders across the business, and manage a growing AI product portfolio. You will ensure that AI initiatives are aligned with the company’s strategic goals, drive operational efficiencies, and foster innovation to deliver breakthrough solutions that have a measurable impact.


Key Responsibilities:

AI Product Strategy & Vision:

  • Lead the AI product strategy, defining clear product roadmaps, aligning AI initiatives with the company’s overall business goals, and identifying market opportunities for AI-driven products and services.

Product Development & Lifecycle Management:

  • Oversee the end-to-end lifecycle of AI products, from concept to launch, and ensure successful integration with existing business operations. Manage the iterative process of product innovation, balancing short-term and long-term goals.

Cross-functional Collaboration:

  • Collaborate with key stakeholders, including senior leadership, engineering, data science, marketing, and sales teams, to ensure alignment and successful execution of AI product initiatives.

Customer-Centric Innovation:

  • Develop a deep understanding of customer needs, behaviors, and pain points to ensure AI products address real-world problems, are user-centric, and deliver measurable value to the business.

Team Leadership & Development:

  • Build and lead a high-performing product team, including AI specialists, product managers, and technical staff. Mentor and develop talent within the team and foster a culture of innovation, collaboration, and accountability.

Metrics & KPIs:

  • Define and track key performance indicators (KPIs) to measure the success of AI products, ensuring that AI-driven solutions are aligned with business outcomes such as revenue growth, cost savings, customer satisfaction, and retention.

Private Equity Partnership:

  • Work closely with private equity stakeholders to provide updates on AI product development, investment plans, and market trends. Communicate the value and strategic impact of AI initiatives to investors and help secure future funding to expand product offerings.

Market Research & Competitive Analysis:

  • Stay up-to-date with emerging AI technologies, industry trends, and competitive landscape to maintain the company’s leadership in AI innovation.


Qualifications:

Experience:

  • 7+ years of experience in product management, with a strong focus on AI or data-driven products in a fast-paced, high-growth environment.
  • Proven track record of successfully leading AI product teams, developing AI solutions, and bringing them to market in a private equity-backed or tech-driven company.
  • Strong experience working with private equity or venture capital stakeholders and understanding the dynamics of growing a business through investment.

Education:

  • Bachelor’s degree in Computer Science, Engineering, Data Science, Business, or a related field. MBA or advanced degree preferred.


Skills & Expertise:

  • Deep understanding of AI technologies, including machine learning, deep learning, natural language processing, and data analytics.
  • Proven ability to translate complex technical concepts into clear business strategies and product roadmaps.
  • Strong leadership, interpersonal, and communication skills with the ability to inspire and influence across all levels of the organization.
  • Excellent analytical and problem-solving skills, with a focus on data-driven decision-making.
  • Familiarity with Agile product development methodologies.

Leadership:

  • A visionary leader who can manage multiple stakeholders, inspire teams, and influence the overall direction of AI strategy within the company.
  • Strong business acumen, capable of balancing product goals with financial, operational, and customer objectives.

Private Equity/Investment Experience:

  • Prior experience working with private equity-backed organizations and an understanding of how to drive value creation within a portfolio company.
  • Comfort with managing high-level strategic discussions with investors and aligning product initiatives to broader business objectives.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.