Reward & Recognition Partner Operations London, UK

Jobleads
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist – GenAI & AI Engineering

Senior Data Scientist - Financial Crime

Machine Learning Engineer III

Machine Learning Engineer III

Machine Learning Engineer II

Machine Learning Engineer II

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

Snapshot

We are looking for ambitious people with a passion for enabling exceptional individuals to join our unique people-first business. Our team’s purpose is to both cultivate and optimise our exceptional talent and build a values driven, highly effective and healthy organisation and culture; an organisation where world-class research and creativity thrive, and doing great work that contributes to our mission is everyone’s number one focus.

About Us

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

The role

You will be joining the Reward & Recognition team, which sits within Talent, Performance & Reward team at Google DeepMind. Your role will be to review, rethink (where it makes sense) and deliver reward processes and programs, and operate our heartbeat activity. You will be managed by the EMEA Reward & Recognition Lead, and collaborate closely with the rest of the Reward & Recognition team in London and the US, alongside the Talent Acquisition team and People & Culture Partners on a daily basis.

Key responsibilities:

  • Contribute to running our compensation programs that support our wider organisational philosophy while ensuring strong governance.
  • Provide advice and counsel to the Talent Acquisition team and hiring managers through the offer negotiation process.
  • Work with compensation metrics, reports, and tools to inform compensation decisions and forecast, report, and/or analyse compensation outcomes.
  • Develop and implement reward and recognition frameworks for the full organisational structure at Google DeepMind.
  • Communicate effectively with senior partners and internal data & analytics teams to provide insights to the people analytics function, enabling insights-led business decisions.
  • Analyse available market data to assess competitiveness of total compensation offerings at all levels of the business.
  • Lead compensation projects from design through execution, including modelling, project planning, communicating, and training in partnership with the rest of the Reward & Recognition team.
  • Continuously evaluate the efficiency of compensation programs, innovating and recommending changes to improve the effectiveness of our investment in total rewards.

About you

In order to set you up for success as a Reward & Recognition Partner at Google DeepMind, we look for the following skills and experience:

  • Experience managing complex reward programs and creating and improving processes. Ideally, this would be in a tech company.
  • Ability to assimilate ambiguous information from a wide variety of sources to provide meaningful and timely insights to inform decisions, and you are comfortable communicating these findings.
  • You can run with a brief and are comfortable dealing with some ambiguity. When something you are working on changes, you adapt comfortably with a readiness to learn.
  • You possess exceptional attention to detail.
  • Proven experience of handling sensitive data with confidentiality and care.
  • You conduct research and test hypotheses to input into formulation of business approaches, and seek out feedback from others in determining priorities.
  • You are proficient using Google Sheets, with strong analytical skills.
  • You are collaborative in your approach to stakeholder management. You regularly form project proposals, including improvements to processes or ways of working.
  • You are highly organised with experience of managing your own workload across an array of activity and projects.
  • Agile and flexible, you are looking to learn from others at every opportunity.
  • You have experience partnering with other teams and have good communication skills, with a natural ability to build relationships across an organisation, instilling trust and creating understanding.

In addition, the following would be an advantage:

  • Having worked in a tech company or company with similar talent challenges (such as unique skills, competition for talent).
  • Enjoy a fast paced environment with frequent change.
  • Experience using SQL and/or other databases to pull data.
  • Knowledge of competitor landscape - be that in terms of other AI research companies or the Bay Area.

#J-18808-Ljbffr

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.