Lead Engineer

Captify
Greater London
1 month ago
Applications closed

Related Jobs

View all jobs

Lead data Engineer - Financial Markets - Day rate

Senior Test Lead Engineer | Network Services

Data Engineer

Lead Electronics Engineer

Lead Data Engineer

Lead Data Engineer - Manchester - Hybrid - £75k - £80k

Lead Engineer - ML and Data Engineering

We are looking for a Lead Engineer with experience in machine learning and a passion for leading people to lead our Contextual Segmentation Team. This team is responsible for bringing the power of search to enhance and create innovative solutions to segment internet pages and domains, as well as TV shows and episodes. To achieve this, you will lead a distributed team that has both software and ML engineers working together from various locations.

Working closely with the rest of the technical leadership team, the successful candidate will be comfortable collaborating with others to design the most effective systems.

Captify’s world-class engineers, semantic specialists and product teams are building the future of Search and as part of our Engineering Team, you will play a key part in developing our offering. We work in small cross-functional teams of developers who focus on a particular product area, and we encourage people to rotate across teams. Our product teams are underpinned and enabled by our platform team.

Our company values are important to us and influence how we work together. With exciting projects, technologies and services in the pipeline now is a great time to be part of our journey.

ABOUT YOU

You are a seasoned Lead Engineer with a strong background in machine learning and a passion for leadership. You have a proven track record of guiding distributed teams, with experience collaborating between software and ML engineers and delivering innovative segmentation solutions. With excellent problem-solving skills and a strategic mindset, you thrive in cross-functional environments, working closely with technical leadership to design and implement effective systems. Not only are you technically proficient but also an inspiring mentor, committed to driving excellence and innovation.

What you’ll be doing:

  • You will help architect and build an innovative search-powered contextual segmentation solution.
  • You will be responsible for designing and building the performance optimisation pipeline for the segmentation solution.
  • You will be responsible for maintaining and developing the Contextual TV solutions in the Company.
  • You will experiment with new tools and technologies to produce cutting-edge solutions to business and engineering problems.
  • You will lead a cross-functional, results-oriented team.
  • You will enable a culture of improvement and innovation in practices, process and technology.
  • Working with data science and the performance team to build hypotheses, design tests, monitoring and KPIs.

What you need to be successful:

  • Have the potential to build and lead a distributed team.
  • Experience with Machine Learning / Data Science.
  • Know our tech stack: Python, Scala, Spark, Flink, Kafka, Kubernetes, Databricks and AWS services.
  • Previous experience with git, CI and CD tools.
  • Passionate about internal quality, good code and effective technical practice.
  • Passion for continuously improving the team’s performance.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Engineering and Information Technology

Industries

Advertising Services

#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.

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

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

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

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.