Lead Data Scientist

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

Job description in French (English version at the bottom)

Lead Data Scientist

Formation

Preferred location: Paris, France

Lexis Nexis 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 Lead 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 participate in 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 have an important role 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

• Contribute to 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.
• 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

• 4+ years of experience building, training, and evaluating Deep Learning and Machine Learning models using tools such as Py Torch ,
  Tensor Flow
  , scikit-learn
  , Hugging Face
  , or Lang Chain
  .
• 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…