Data Scientist

The lead agency
Liverpool
1 week ago
Create job alert

The Role

At TLA, we’re proud to be consumer champions in the automotive space. We’re constantly exploring smarter ways to connect people with the right cars — and that’s where data science plays a key role. As part of the team, you’ll build predictive models that surface insights to improve everything from lead quality and conversion to forecasting. In the short term, you’ll likely focus on performance marketing, where there’s real opportunity to optimise and scale our efforts. You could also get involved in evolving some of our current projects — from refining lead scoring models to improving AI-powered chatbots and developing our bespoke name validation tech.

Why Join TLA?

TLA is a fast-moving, innovative digital business that partners with some of the biggest automotive brands—including the Volkswagen Group, BMW Group, and Ford. Founded over 20 years ago, and with long standing team members we’ve built a close-knit, ambitious team that’s passionate about pioneering technology to drive car sales.

We offer a supportive and collaborative environment, where you’ll have the opportunity to grow and make an impact. Our hybrid model (2 days per week in our fantastic Liverpool city centre office) enables in-office teamwork and collaboration. We’re a highly driven bunch that believes in respect, hard work, and giving back through charitable events and sporting efforts—everything from hiking to skydiving!

What you will be doing:

You’ll play a key role in optimising our marketing strategy by:

• Building and refining marketing mix models (MMM) to help optimise performance across channels and campaigns.

• Forecast delivery limits, costs, and revenue to support smarter planning and budget allocation.

• Develop predictive and statistical models that fuel marketing decisions and unlock growth opportunities.

• Work hands-on with Python to analyse data, develop solutions, and automate key processes.

• Leverage the Azure cloud platform to deploy and maintain models and pipelines.

• Collaborate with marketing, analytics, and engineering teams to turn data into actionable strategies.

What you’ll need to Succeed in the Role:

•Proven track record of applying data science solutions in either an industry or academic (e.g. PhD) setting.

•Solid grasp of core data science methodologies, including machine learning algorithms and statistical analysis.

•Skilled in developing machine learning workflows using Python.

•Passionate about working closely with non-technical teams and promoting a culture of scientific rigor.

•Experience contributing to data science initiatives alongside cross-functional teams, including software engineering professionals.

•Be located within a 1hr commute to Liverpool city centre.

Nice-To-Have Skills:

•Any experience in marketing data science i.e., marketing mixed modelling, demand forecasting etc.

•Experience with Microsoft Azure.

•Experience presenting back data to non-technical stakeholders in a simple and understandable way.

•Any experience with natural language processing (NLP).

Benefits

• Hybrid & flexible working – 2 days per week in the Liverpool office (Monday & Tuesday)

• Competitive salary

• Annual company-wide bonus scheme

• Up to £500 annual training budget

• Private health insurance

• Pension plan

• Cycle to work program

• Extensive activity package including charity focused sporting challenges and fun social events

Want to help shape the future of car buying? Then join TLA! We're looking for people who value teamwork, creativity, and always striving for better. Apply now to be part of our team!

PLEASE NOTE: This role is only open to those with the right to work in the UK without the need for sponsorship or visa, now or in the future. Additionally, candidates must be located within a reasonable commuting distance to our Liverpool city centre office.

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist - Production

Data Scientist/Machine Learning Engineer - RNA Design

Data Scientist - active NPPV3 required

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.

Portfolio Projects That Get You Hired for AI Jobs (With Real GitHub Examples)

In the fast-evolving world of artificial intelligence (AI), an impressive portfolio of projects can act as your passport to landing a sought-after role. Even if you’ve aced interviews in the past, employers in AI and machine learning (ML) are increasingly asking candidates to demonstrate hands-on experience through the projects they’ve built and shared online. This is because practical ability often speaks volumes about your suitability for a role—far more than any exam or certification alone could. In this article, we’ll explore how to build an outstanding AI portfolio that catches the eye of recruiters and hiring managers, including: Why an AI portfolio is crucial for job seekers. How to choose AI projects that align with your target roles. Specific project ideas and real GitHub examples to help you stand out. Best practices for showcasing your work, from writing clear READMEs to using Jupyter notebooks effectively. Tips on structuring your GitHub so that employers can instantly see your value. Moreover, we’ll discuss how you can use your portfolio to connect with top employers in AI, with a handy link to our CV-upload page on Artificial Intelligence Jobs for when you’re ready to apply. By the end, you’ll have a clear roadmap to building a portfolio that will help secure interviews—and the AI job—of your dreams.

AI Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

In today's competitive AI job market, nailing a technical interview can be the difference between landing your dream role and getting lost in the crowd. Whether you're looking to break into machine learning, deep learning, NLP (Natural Language Processing), or data science, your problem-solving skills and system design expertise are certain to be put to the test. AI‑related job interviews typically involve a range of coding challenges, algorithmic puzzles, and system design questions. You’ll often be asked to delve into the principles of machine learning pipelines, discuss how to optimise large-scale systems, and demonstrate your coding proficiency in languages like Python, C++, or Java. Adequate preparation not only boosts your confidence but also reduces the likelihood of fumbling through unfamiliar territory. If you’re actively seeking positions at major tech companies or innovative AI start-ups, then check out www.artificialintelligencejobs.co.uk for some of the latest vacancies in the UK. Meanwhile, this blog post will guide you through 30 real coding & system-design questions you’re likely to encounter during your AI job interview. This list is designed to help you practise, anticipate typical question patterns, and stay ahead of the competition. By reading through each question and thinking about the possible approaches, you’ll sharpen your problem-solving skills, time management, and critical thinking. Each question covers fundamental concepts that employers regularly test, ensuring you’re well-equipped for success. Let’s dive right in.

Negotiating Your AI Job Offer: Equity, Bonuses & Perks Explained

Artificial intelligence (AI) has proven itself to be one of the most transformative forces in today’s business world. From smart chatbots in customer service to predictive analytics in finance, AI technologies are reshaping how organisations operate and innovate. As the demand for AI professionals grows, so does the complexity of compensation packages. If you’re a mid‑senior AI professional, you’ve likely seen job offers that include far more than just a base salary—think equity, bonuses, and a range of perks designed to entice you into joining or staying with a company. For many, the focus remains squarely on salary. While that’s understandable—after all, your monthly take‑home pay is what covers day-to-day expenses—limiting your negotiations to salary alone can leave considerable value on the table. From stock options in ambitious startups to sign‑on bonuses that ‘buy you out’ of your current contract, modern AI job offers often include elements that can significantly boost your long-term wealth and job satisfaction. This article aims to shed light on the full scope of AI compensation—specifically focusing on how equity, bonuses, and perks can enhance (or sometimes detract from) the overall value of your package. We’ll delve into how these elements work in practice, what to watch out for, and how to navigate the negotiation process effectively. Our goal is to provide mid‑senior AI professionals with the insights and tools to land a holistic compensation deal that accurately reflects their technical expertise, leadership potential, and strategic importance in this fast-moving field. Whether you’re eyeing a leadership role in machine learning at an established tech giant, or you’re considering a pioneering position at a disruptive AI startup, the knowledge in this guide will help you weigh the merits of base salary alongside the potential riches—and risks—of equity, bonuses, and other benefits. By the end, you’ll have a clearer sense of how to align your compensation with both your immediate lifestyle needs and long-term career aspirations.