Applied Data Scientist

causaLens
1 year ago
Applications closed

Related Jobs

View all jobs

Applied Data Scientist

Applied Data Scientist

Applied Data Scientist

Applied Data Scientist: AI/ML for Real-World Impact Hybrid

Applied Data Scientist — End-to-End ML in Production (Hybrid)

Applied Data Scientist

causaLens is pioneering the world’s first platform for building AI data scientists - empowering everyone to create and deploy their own data science agents in days. Our platform enables teams to collaborate in a multi-agent environment, ensuring human oversight across the entire workflow and making AI-powered data science trustworthy and accessible to everyone – from analysts to business leaders. We power industry leaders, including Cisco, Johnson & Johnson, Canon, and McCann Worldgroup, to accelerate and scale their data science capability. Join us to build the World’s First Platform for AI Data Scientists.What we are looking forWe are looking for a Senior Data Scientist based in London to join our mission to create AI Data Scientists to radically advance decision-making for leading enterprises. You will join a team of 9 Data Scientists. We hire the top 1% of Data Science talent to create an intellectually stimulating environment where you can thrive and learn. You will be helping leading enterprises build their custom data science agents and help our users get the maximum out of the platform. What you will doAs a Senior Data Scientist at causaLens, you will play a pivotal role in advancing our decision-making technology. This position demands a strong foundation in data science, particularly but not limited to time series, and using Python as the primary programming language. Some of your responsibilities will include:Using our Agentic AI framework to build data science solutions and models, using our platform on client-supplied data sets and use cases.Collaborating directly with business stakeholders to integrate domain knowledge into the modelling process, demonstrating how insights can enhance decision workflows.Crafting long-term visions and plans, in collaboration with clients and causaLens stakeholders, to successfully deploy agentic workflows into customers' strategies.Work closely with the product and engineering teams to shape the development of our platform.Communicate technical topics to non-technical audiences.RequirementsAt least 2 years of commercial data science experience using PythonPlease note that this and the following bullet imply a significant breadth and depth of technical skills we will be testing for during the interview process - e.G., Statistics; other programming/scripting languages; solid understanding andexperience with Cloud technologies; OOP, TDD, GitHub/Actions/Flow, and MLOps best practices; classical ML algorithms; at least some NLP, etc. Strong academic record in a quantitative field (MEng, MSci, EngD or PhD)Excellent and proven communication and teamwork skillsPrevious experience in high-growth technology companies or technical consultancy is a plusPrevious experience in sales, pre-sales, and/or other technical evangelism is a plusExperience with consulting and/or customer-facing roles, especially in the supply chain, demand forecasting, retail/cpg, manufacturing, marketing, financial services, or the public sector is a plusExperience with LLM and RAG, GenAI, and agentic workflows is a plusBenefitsWe 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 holidaysbuy/sell holiday options (up to 5 days)Share optionsPension schemeHappy hours and team outingsReferral bonus programCycle to work schemeFriendly tech purchasesBenefits to choose from include Health/Dental InsuranceSpecial DiscountsLearning and development budgetWork abroad daysOffice snacks and drinksLogisticsOur interview process consists of a few screening interviews and a "Day 0" which is spent with the team (in-office). We will always be as transparent as possible so please don’t hesitate to reach out if you have any questions.If you require accommodations during the application process or in your role at causaLens, please contact us at

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.