Principal Data Scientist (H/F)

LexisNexis Risk Solutions
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist

Principal Data Scientist

Principal Data Scientist: Scale ML for Audiences (Hybrid)

Principal Data Scientist London, United Kingdom

Principal Data Scientist: ML Leader, Mentor, Hybrid Role

Principal Data Scientist: AI for Content Creation

Data, Research & Analytics
Principal Data Scientist (H/F)

Job Description in French (English version at the bottom)


Preferred location: Paris, France


LexisNexis Risk Solutions is a global leader in technology and data analytics, tackling some of the world’s most complex and meaningful challenges — from stopping cybercriminals to enabling frictionless experiences for legitimate consumers.


As a Principal Data Scientist, you will play a key role in shaping the future of our AI capabilities across multiple products within our Fraud, Identity, and Financial Crime Compliance portfolio. You will lead the ideation, research, modeling, and implementation of new AI-driven features — with a strong focus on Large Language Models (LLMs), Generative AI, and advanced Machine Learning.


Your work will directly impact millions of identity verifications and fraud prevention decisions every day, helping global organizations operate safely and efficiently. You will also contribute to the company’s long‑term AI strategy and act as a thought leader and role model within the data science teams.


Operating within a global organization, you will collaborate closely with engineering labs, analytics teams, and professional services, while staying attuned to customer feedback and business priorities. A strong business mindset, proactive communication, and ability to drive innovation across teams are key to success in this role. You will work primarily on a European schedule but engage frequently with colleagues across multiple time zones and travel when needed.


Key Responsibilities

Lead the research, prototyping, and productionization of new AI and ML features across our product portfolio.


Partner with Product Managers and Engineering teams to design and deliver impactful, data-driven enhancements.


Deeply understand existing products and data assets to identify opportunities for AI‑driven improvement.


Design and execute experiments to validate new research ideas and evaluate model performance.


Train, fine‑tune, and optimize LLM and ML models on structured and unstructured data derived from APIs and customer workflows.


Develop strategies for real‑time model inference and scalable deployment.


Collaborate with external vendors for data collection, annotation, and research initiatives.


Engage with customers and regional professional services teams to understand evolving fraud patterns and integrate insights into product development.


Mentor and support other data scientists, fostering technical excellence and innovation across the organization.


Education

Master’s degree or PhD in Computer Science, Artificial Intelligence, Applied Mathematics, or a related field.


Degree from a leading Engineering School (Grand Ecole) or University with a strong quantitative curriculum is highly valued.


Requirements

8+ years of experience building, training, and evaluating Deep Learning and Machine Learning models using tools such as PyTorch, TensorFlow, scikit‑learn, HuggingFace, or LangChain.


Experience in a start‑up or a cross‑functional team is a plus


Experience in Natural Language Processing (NLP) is a plus


Strong programming skills in Python, including data wrangling, analysis, and visualization.


Solid experience with SQL and database querying for data exploration and preparation.


Familiarity with cloud platforms (AWS, Azure, …) and modern data stack tools (Snowflake, Databricks, …)


Proven ability to tackle ambiguous problems, develop data‑informed strategies, and define measurable success criteria.


Familiarity with object‑oriented or functional programming languages such as C++, Java, or Rust is a plus


Experience with software engineering tools and practices (e.g. Docker, Kubernetes, Git, CI/CD pipelines) is a plus.


Knowledge of ML Ops, model deployment, and monitoring frameworks.


Understanding of fraud prevention, authentication, or identity verification methodologies is a plus.


Excellent communication skills with both technical and non‑technical stakeholders.


Strong English proficiency (C1/C2) and proven experience working in multicultural, international environments.


Ability to collaborate across time zones and travel occasionally as required.


Why Join Us

At LexisNexis Risk Solutions, you’ll join a global community of innovators using AI to make the world a safer place. You’ll have the autonomy to explore new ideas, the resources to bring them to life, and the opportunity to shape how AI transforms fraud and identity verification on a global scale.


Additional location(s)

Wales; UK - London (Bishopsgate)


We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120.


Criminals may pose as recruiters asking for money or personal information. We never request money or banking details from job applicants. Learn more about spotting and avoiding scams here.


Please read our Candidate Privacy Policy.


USA Job Seekers:


We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. EEO Know Your Rights.


#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.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.