Talent Acquisition Partner

Tapestry Venture Capital, LLC
London
2 months ago
Applications closed

Related Jobs

View all jobs

Demand Planner

Product Quality Non-Conformance Engineer

Data Analyst / Business Analyst – Risk Rating & Pricing

Bureau Data Analyst

Senior Security Architect

Data Scientist

As a Talent Acquisition Partner, you will play a critical role in scaling Zapp’s tech workforce by identifying, engaging, and hiring the best talent in software development, cloud computing, artificial intelligence, machine learning, and other high-impact technical domains. You will bring deep expertise in talent acquisition strategies, market insights, and employer branding while ensuring a seamless candidate experience.

About Zapp:

Zapp is London’s leading premium convenience retail platform. Founded in 2020, our vision is to disrupt the multi-trillion dollar convenience retail market, currently dominated by major players, by developing best-in-class customer-centric technology and fulfilment solutions. Zapp partners with some of the world’s leading brands to deliver an exclusive range of hand-picked products 24/7, delivered in minutes.

Key Responsibilities

Strategic Recruitment & Hiring:

  1. Own and drive the end-to-end recruitment process for technical roles across Zapp.
  2. Partner closely with hiring managers and senior leadership to understand business needs, hiring goals, and workforce planning.
  3. Develop and execute sourcing strategies to attract top technical talent, including passive candidates.
  4. Utilise data to drive hiring decisions whilst leveraging recruiting metrics, pipeline data, and market insights to refine hiring strategies.
  5. Ensure a diverse and inclusive hiring process, proactively engaging underrepresented talent pools.

Stakeholder & Candidate Management:

  1. Build strong relationships with hiring managers, HR business partners, and technical teams to align recruitment efforts with business objectives.
  2. Provide interview training and best practices guidance to hiring teams.
  3. Act as a trusted advisor to candidates throughout the hiring process, ensuring a positive candidate experience.
  4. Negotiate offers and ensure seamless onboarding in collaboration with HR teams.
  5. Utilise advanced sourcing tools and techniques, including Boolean searches, LinkedIn Recruiter, GitHub, Stack Overflow, and AI-driven sourcing tools.
  6. Conduct market research to understand hiring trends, salary benchmarks, and competitor intelligence.
  7. Attend and organise tech hiring events, hackathons, and university partnerships to engage with top-tier talent.

Process Optimisation & Employer Branding

  1. Identify and implement process improvements to enhance recruitment efficiency and candidate experience.
  2. Leverage recruitment technologies, including ATS (Teamtailor), and CRM systems.
  3. Collaborate with employer branding teams to create compelling job advertisements, social media campaigns, and talent engagement strategies.
  4. Contribute to internal hiring projects, such as DEI (Diversity, Equity & Inclusion) initiatives, recruiter training, and interview panel calibration.

Qualifications & Experience

Basic Qualifications:

  1. 5+ years of experience in full-cycle recruitment, with a focus on technical hiring (e.g., software engineers, data scientists, DevOps, cloud architects, AI/ML specialists).
  2. Proven track record in headhunting, sourcing, and engaging passive candidates.
  3. Experience using Applicant Tracking Systems (ATS) and recruitment tools such as LinkedIn Recruiter, Teamtailor, Pinpoint etc.
  4. Strong awareness of technical skill sets, programming languages, and IT infrastructure.
  5. Excellent stakeholder management skills and the ability to influence hiring decisions.
  6. Ability to analyse recruitment metrics and make data-driven hiring decisions.
  7. Strong negotiation, communication, and relationship-building skills.

Preferred Qualifications:

  1. Experience working in a high-volume, fast-paced environment, preferably in a tech-driven or global organisation.
  2. Strong knowledge of DEI hiring best practices and initiatives.
  3. Familiarity with global tech hiring trends, university recruiting, and employer branding strategies.
  4. Professional certifications in Recruitment, Talent Acquisition, or HR (e.g., AIRS, LinkedIn Certified Recruiter, SHRM-CP, PHR) while not essential, will be advantageous.

Department:People & Org

Locations:HQ

Employment type:Full-time

Talent Acquisition PartnerAlready working at Zapp?

Let’s recruit together and find your next colleague.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.

Machine Learning Jobs in the Public Sector: Opportunities Across GDS, NHS, MOD, and More

Machine learning (ML) has rapidly moved from academic research labs to the heart of industrial and governmental operations. Its ability to uncover patterns, predict outcomes, and automate complex tasks has revolutionised industries ranging from finance to retail. Now, the public sector—encompassing government departments, healthcare systems, and defence agencies—has become an increasingly fertile ground for machine learning jobs. Why? Because government bodies oversee vast datasets, manage critical services for millions of citizens, and must operate efficiently under tight resource constraints. From using ML algorithms to improve patient outcomes in the NHS, to enhancing cybersecurity within the Ministry of Defence (MOD), there’s a growing demand for skilled ML professionals in UK public sector roles. If you’re passionate about harnessing data-driven insights to solve large-scale problems and contribute to societal well-being, machine learning jobs in the public sector offer an unparalleled blend of challenge and impact. In this article, we’ll explore the key reasons behind the public sector’s investment in ML, highlight the leading organisations, outline common job roles, and provide practical guidance on securing a machine learning position that helps shape the future of government services.

Contract vs Permanent Machine Learning Jobs: Which Pays Better in 2025?

Machine learning (ML) has swiftly become one of the most transformative forces in the UK technology landscape. From conversational AI and autonomous vehicles to fraud detection and personalised recommendations, ML algorithms are reshaping how organisations operate and how consumers experience products and services. In response, job opportunities in machine learning—including roles in data science, MLOps, natural language processing (NLP), computer vision, and more—have risen dramatically. Yet, as the demand for ML expertise booms, professionals face a pivotal choice about how they want to work. Some choose day‑rate contracting, leveraging short-term projects for potentially higher immediate pay. Others embrace fixed-term contract (FTC) roles for mid-range stability, or permanent positions for comprehensive benefits and a well-defined career path. In this article, we will explore these different employment models, highlighting the pros and cons of each, offering sample take‑home pay scenarios, and providing insights into which path might pay better in 2025. Whether you’re a new graduate with a machine learning degree or an experienced practitioner pivoting into an ML-heavy role, understanding these options is key to making informed career decisions.