Senior Talent Acquisition Partner - Data (12 month FTC)

relaytech.co
London
1 week ago
Applications closed

Related Jobs

View all jobs

Head of Business Resource Management

Senior Applied Scientist - Computer Vision

Head of Sales (EMEA)

Regional Finance Director

Finance Director

Senior Data Engineer

Company Mission

In the future, almost everything we consume will simply materialise on our doorsteps – what we call “e-commerce” today will simply be “commerce” tomorrow. But if we continue on today’s trajectory, the growth of e-commerce risks damaging the environment, alienating our communities, and straining the bottom line for small businesses.

Relay is an e-commerce-native logistics network. We are built from the ground up for environmental, social, and economic sustainability. By building from the ground up we are able to entirely rethink both the middle and last mile enabling us to reduce the number of miles driven to deliver each parcel, lower carbon emissions, and lower costs, all while channelling funds to community members.

At the same time, we’re fixing the last broken aspect of e-commerce for consumers: delivery. As shoppers, we should have complete control over when and how we receive our purchases, and we should be able to return unwanted items as easily as we ordered them. That’s why whenever you buy from a merchant powered by Relay, you’ll be able to reschedule your delivery at any time. And if you don’t like what you ordered, at the tap of a button we’ll send someone to pick it up.

To orchestrate this complex ballet, Relay relies on a wide range of technologies, from advanced routing and planning to sophisticated user experiences that guide our team members on the ground.

As aSenior Talent Acquisition Partner – Data, you will be instrumental in building out Relay’s data science and analytics capabilities. You will partner closely with hiring managers, executives, and the data team to ensure that we attract, hire, and retain the best talent in the field. This is a unique opportunity to shape the future of Relay’s talent acquisition strategy specifically focused on the recruitment of data scientists, data analysts, machine learning engineers, and other data-driven roles.

Over the next 6-12 months, you will become the go-to expert for all things related to data science hiring, from sourcing and interviewing to onboarding and reporting. You’ll play a pivotal role in establishing scalable processes that will enable Relay to build a world-class data team.

Key Responsibilities

  1. End-to-End Recruitment:Lead the hiring process for data science & analytics roles, from sourcing and screening candidates to extending offers. Ensure a smooth, positive experience for candidates and hiring managers.

  2. Interviewer Coaching & Training:Coach and mentor hiring managers and interviewers on best practices for assessing technical and soft skills in data science candidates. Train interviewers on effective evaluation techniques and how to assess technical proficiency, problem-solving skills, and cultural fit.

  3. Stakeholder Management:Build strong relationships with hiring managers, data science leaders, and the People Team to align hiring strategies with business needs. Collaborate with senior leadership to forecast hiring needs and develop strategies to meet them.

  4. Data-Driven Recruitment:Use your analytical skills to track and report on key hiring metrics, such as time to hire, conversion rates, candidate experience, and offer acceptance rates. Utilize data to continuously improve the recruitment process and make data-driven decisions.

  5. Talent Sourcing & Networking:Leverage your network and platforms like LinkedIn, GitHub, Kaggle, and Stack Overflow to identify and engage top-tier data science and analytics talent. Build long-term relationships with candidates and create talent pipelines for future hiring needs.

  6. Continuous Learning & Development:Stay up-to-date with the latest trends and technologies in the data science field. Bring insights and knowledge to the recruitment process to ensure we are attracting the best talent in an ever-evolving market.

Qualifications and Experience

We are looking for candidates with:

  1. Proven experiencein talent acquisition, specifically focused on data science or other technical roles (machine learning, AI, data engineering).

  2. Strong knowledgeof the data science landscape, including the technical skills and experience required for various data science positions (e.g., Python, R, machine learning algorithms, data visualization, etc.).

  3. Familiarity with technical recruitment processes, particularly for evaluating candidates' problem-solving abilities, coding skills, and technical proficiency in the data science domain.

  4. Proficiency with recruitment toolssuch as LinkedIn Recruiter, ATS, and sourcing platforms like Kaggle and GitHub.

  5. Experience in data-driven decision-making, using recruitment analytics to optimize the hiring process.

  6. Excellent communication and interpersonal skills, with the ability to build relationships and influence at all levels of the organization.

  7. Experience working with cross-functional teamsto align talent acquisition strategies with business goals and data science needs.

  8. Agrowth mindset, with a passion for learning, innovation, and continuously improving the recruitment process.

The qualifications and experiences above act as a loose guide to what we’re looking for. We’d still love to hear from you if you have more or less experience, so long as the core skills can be demonstrated.

Relay is offering

  1. 25 days annual leave per year (plus bank holidays).

  2. Generous equity package.

  3. Bupa Global: Business Premier Health Plan - Comprehensive global health insurance with direct access to specialists, dental care, mental health support and more.

  4. Contributory pension scheme.

  5. Hybrid working

  6. Free membership of the gym in our co-working space in London.

  7. Cycle-to-work scheme

  8. A culture of learning and growth, where you're encouraged to take ownership from day one.

  9. Plenty of team socials and events - from pottery painting to life-size Monopoly and escape rooms

#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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.