AI Engineer - Data Science

causaLens
City of London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Value Engineer - Data Engineering

Data Engineer (AI Analytics and EdTech Developments)

Full Stack Data Engineer

Data Engineer

Data Engineer

Data Engineer

Overview

causaLens delivers Digital Workers that enterprises can truly rely on. Soon, competing without Digital Workers will be impossible. We’ve built the first factory and Operating System for creating, deploying, and governing Digital Workers. For too long, enterprises have been bogged down by repetitive work, an overload of tools, and costly consultancies. It’s time to simplify. It’s time for Digital Workers to take on the repetitive workflows, freeing humans to focus on what matters most. Trusted by leading companies like J&J, Cisco, IPG Group, and Syneos Health. Backed by over $50M in funding from world-class investors, including Molten Ventures (formerly Draper Esprit), Dorilton Capital, and IQ Capital, plus visionary angel investors such as the CEO of Revolut.

Here are 2 articles that define our culture: 1. A Hiring Framework for a New Kind of Services Company. The Primacy of Winning.

We are seeking AI Engineers with strong data science expertise who are passionate about helping world-leading enterprises put cutting-edge AI agents into production. You’ll work on impactful, high-visibility projects - designing, building, and delivering intelligent solutions that solve real business problems at scale.

What you’ll bring:

  • experience with traditional data science and machine learning (solid stats, programming, ideally exposure to MLOps, etc.)

  • Hands-on experience building production-grade solutions using LLMs, RAGs, MCPs, and agentic workflows.

  • Client-facing experience with a forward-deployed engineering mindset. You’ll work directly with both technical teams and business stakeholders to understand real-world challenges and deliver solutions that drive measurable impact.

  • Strong solution architecture and delivery skills: ability to translate complex business problems into scalable, intelligent AI solutions.

What you’ll do:

  • Collaborate directly with top-tier enterprises to understand needs, design and deploy bespoke agentic workflows.

  • Design and implement robust architectures that leverage the latest AI technologies.

  • Lead the delivery of high-impact AI products from concept to deployment.

You’ll collaborate with top-tier enterprises to design and deploy bespoke data science agents, empowering users to fully leverage the capabilities of our platform.

Benefits

We care about our people’s lives, both inside and outside of causaLens. Beyond the core benefits like competitive remuneration and a good work-life balance, we offer the following:

  • 25 days of paid holiday, plus bank holidays

  • carry over/sell holiday options (up to 5 days)

  • Share options

  • Pension scheme

  • Happy hours and team outings

  • Referral bonus program

  • Cycle to work scheme

  • Friendly tech purchases

  • Benefits to choose from, including Health/Dental Insurance

  • Special Discounts

  • Learning and development budget

  • Work abroad days

  • Office snacks and drinks

LogisticsOur interview process consists of screening sessions with the hiring manager and a Day 0 which involves an approx 3 hours in-person challenge followed by an in-person presentation and interviews. Have questions? We encourage open dialogue—reach out anytime.

If you require accommodations during the application process or in your role at causaLens, please contact us at


#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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.