Lead Data Scientist - UK 12 Month FTC

CI&T
London
9 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

We aretech transformationspecialists, uniting human expertise with AI to create scalable tech solutions.

With over 6,500 CI&Ters around the world, we’ve built partnerships with more than 1,000 clients during our 30 years of history. Artificial Intelligence is our reality.

General Description:

We are looking for scientists who are passionate about data and are eager to tackle big challenges using Data Science and Machine Learning. The main focus of this role is to solve non-trivial business problems in Fortune 500 companies. This person will mostly work with a mix of structured and unstructured data, using scientific methods and state-of-the-art techniques and tools to help our customers achieve their business objectives.

Responsibilities:

  1. Understand complex business problems and translate them into structured data problems.
  2. Capture and explore complex data sets (structured and unstructured data).
  3. Prototype models of different complexity (business analysis, statistical models, machine learning) using modern data science tools (Notebooks, Clouds).
  4. Design and implement machine learning models, metrics, and application of feature engineering techniques applied to customer problems.
  5. Support pre-sales in business opportunities and engineering teams in the implementation of production-ready solutions involving machine learning.
  6. Evaluate hypotheses and the impact of machine learning algorithms on key business metrics. Simulations and offline/online experimentation (via A/B tests) is part of the game.
  7. Research and understand user behavior patterns, such as user engagement and segmentation, using machine learning models to help test hypotheses.
  8. Communicate findings effectively to an audience of engineers and executives.

Required Qualifications:

  1. Bachelor’s Degree in Computer Science/Engineering, Applied Math, Statistics, Physics or other related quantitative areas.
  2. Advanced oral and written communication skills in English.
  3. Ability to understand mathematical models and algorithms in research papers and implement them into running software for Proof-of-Concepts and projects.
  4. Ability to explore big data without a specific problem defined, in order to come up with the right questions and provide interesting findings.
  5. Ability to provide visibility of the progress of tasks to the team by means of small deliverables.
  6. Proficient in computer languages like Python or R, and SQL, making use of the best frameworks for machine learning pipelines, data visualization, manipulation, transforming, models training and evaluation, and models deployment.
  7. Experience with common feature engineering techniques and machine learning algorithms for Supervised and Unsupervised Learning, like Regression, Classification, Clustering, Dimensionality Reduction, Association Rules, Ranking, and Recommender Systems.
  8. Experience with Natural Language Processing (NLP and NLU).
  9. Experience using Generative AI systems (e.g. ChatGPT) and best practices (e.g. Prompt Engineering).
  10. Understanding the key concepts on how to apply Generative AI in building RAG solutions (embeddings, dense search).
  11. Business sense and consulting behavior to identify and breakdown problems, define and evaluate hypotheses.
  12. Think critically and act in a detail-oriented fashion while keeping the 'big picture' in mind.
  13. Ability to provide creative and innovative approaches to problem solving.
  14. Ability to work independently and within a collaborative team environment.

Desired Qualifications:

  1. Masters or PhD in Machine Learning / Data Mining / Statistics.
  2. Experience in building advanced Information Retrieval or Question Answering systems using NLP and Generative AI techniques (e.g. RAG and GraphRAG).
  3. Experience with construction and integration of Knowledge Graphs.

Collaboration is our superpower, diversity unites us, and excellence is our standard. We value diverse identities and life experiences, fostering a diverse, inclusive, and safe work environment. We encourage applications from diverse and underrepresented groups to our job positions.

Seniority Level

Not Applicable

Employment Type

Full-time

Job Function

Engineering and Information Technology

Industries

IT Services and IT Consulting

#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 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.