Data Scientist - Fraud

Data Scientist - Fraud

London Full-Time 36000 - 60000 £ / year (est.) No home office possible
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At a Glance

  • Tasks: Analyze data and develop machine learning models to combat fraud.
  • Company: Join LexisNexis Risk Solutions, a leader in risk assessment and fraud prevention.
  • Benefits: Enjoy a dynamic work environment with opportunities for innovation and collaboration.
  • Why this job: Make a real-world impact by protecting billions in revenue while enhancing customer experiences.
  • Qualifications: Experience in data science, proficiency in Python and SQL, and strong analytical skills required.
  • Other info: Work with cutting-edge technology on a global scale in a fast-paced team.

The predicted salary is between 36000 - 60000 £ per year.

Job Description

About the business: LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below,

About the team: You will be part of a team who use global data from the largest real-time fraud detection platform to craft solutions for our enterprise customers.

About the role: Your experience with data analysis, statistical modelling, and machine learning will lead to immediate real-world impact in the form of lower customer friction, reduced fraud losses and as a result, increased customer profitability. You’ll leverage a real-time platform analysing billions of transactions per month for some of the largest companies operating in Financial Services, Insurance, e-Commerce, and On-Demand Services. These tools will allow you to attain a unique perspective of the Internet, and every persona connected to it. On top of driving innovation projects, you’ll be continually collaborating with internal product and engineering teams, customer-facing account teams, and external business leaders and risk managers. The comprehensive models you build will go head-to-head against some of the most motivated attackers in the world to protect billions in revenue.

Responsibilities:

  • Scoping, developing, and implementing machine learning or rule-based models following best practice, to banking model governance standards
  • Using your strong knowledge of SQL and Python plus quantitative skills to define features that capture evolving fraudster behaviours
  • Develop internal tools to streamline the model training pipeline and analytics workflows
  • Appling your curiosity and problem-solving skills to transform uncertainty into value-add opportunities
  • Using your strong attention to detail and ability to craft a story through data, delivering industry-leading presentations for external and executive audiences
  • Building an extensive knowledge of cybercrime – account takeover, scams, social engineering, Card Not Present (CNP) fraud, money laundering and mule fraud etc
  • Employing your multi-tasking and prioritisation skills to excel in a fast-paced environment with frequently changing priorities

Requirements:

  • Experience in a data science role, ideally within the fraud, risk, or payments domain
  • Proficiency in Python and SQL (BI tools such as SuperSet, Tableau or PowerBI is a bonus)
  • Hands-on experience in machine learning model development, evaluation, and production deployment, with familiarity in MLOps principles to build scalable and standardised workflows and implement effective ML monitoring systems
  • Proven ability to create polished presentations and effectively communicate insights to customers with attention to detail
  • Have extensive multi-tasking and prioritisation skills. Needs to excel in fast paced environment with frequently changing priorities

Learn more about the LexisNexis Risk team and how we work here

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Data Scientist - Fraud employer: ZipRecruiter

At LexisNexis Risk Solutions, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Data Scientist in our dynamic team, you'll have the opportunity to work with cutting-edge technology and global data, driving impactful solutions that protect billions in revenue while enhancing your professional growth through continuous learning and development. Our commitment to employee well-being is reflected in our comprehensive benefits package and a supportive environment that encourages creativity and teamwork.
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Contact Detail:

ZipRecruiter Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Data Scientist - Fraud

✨Tip Number 1

Familiarize yourself with the latest trends in fraud detection and prevention. Understanding the current landscape of cybercrime, including account takeover and money laundering, will help you speak knowledgeably during interviews and demonstrate your passion for the field.

✨Tip Number 2

Showcase your technical skills by working on personal projects or contributing to open-source projects that involve machine learning and data analysis. This hands-on experience will not only enhance your resume but also give you concrete examples to discuss during your interview.

✨Tip Number 3

Network with professionals in the fraud and risk management space. Attend industry conferences, webinars, or local meetups to connect with others in the field. These connections can provide valuable insights and potentially lead to referrals for job openings.

✨Tip Number 4

Prepare to discuss your experience with SQL and Python in detail. Be ready to explain how you've used these tools in past projects, particularly in developing machine learning models or analyzing large datasets, as this will be crucial for the role.

We think you need these skills to ace Data Scientist - Fraud

Data Analysis
Statistical Modelling
Machine Learning
SQL Proficiency
Python Proficiency
Feature Engineering
Model Governance Standards
MLOps Principles
Model Development and Evaluation
Production Deployment
Effective Communication Skills
Presentation Skills
Attention to Detail
Problem-Solving Skills
Curiosity
Multi-tasking
Prioritisation Skills
Knowledge of Cybercrime
Experience in Fraud or Risk Domain

Some tips for your application 🫡

Understand the Role: Take the time to thoroughly read the job description and understand the specific requirements and responsibilities of the Data Scientist - Fraud position. Tailor your application to highlight relevant experiences and skills that align with the role.

Highlight Technical Skills: Make sure to emphasize your proficiency in Python and SQL, as well as any experience with machine learning model development. Provide specific examples of projects or tasks where you utilized these skills effectively.

Showcase Problem-Solving Abilities: In your application, include examples that demonstrate your curiosity and problem-solving skills. Discuss how you've transformed uncertainty into valuable insights or solutions in previous roles.

Craft a Compelling Presentation: Since the role involves delivering presentations, consider including a brief example of a presentation you've created in the past. Highlight your ability to communicate complex data insights clearly and effectively to various audiences.

How to prepare for a job interview at ZipRecruiter

✨Showcase Your Technical Skills

Be prepared to discuss your experience with Python and SQL in detail. Bring examples of projects where you've implemented machine learning models or developed analytics workflows, as this will demonstrate your hands-on expertise.

✨Understand the Fraud Landscape

Familiarize yourself with various types of fraud, such as account takeover and money laundering. Being able to discuss these topics intelligently will show your commitment to understanding the industry and its challenges.

✨Prepare for Problem-Solving Questions

Expect to face scenario-based questions that assess your problem-solving skills. Practice articulating how you would approach transforming uncertainty into actionable insights, as this is crucial for the role.

✨Craft a Compelling Story with Data

Since you'll need to present findings to both technical and non-technical audiences, practice how to effectively communicate complex data insights. Use storytelling techniques to make your presentations engaging and clear.

Data Scientist - Fraud
ZipRecruiter
Z
  • Data Scientist - Fraud

    London
    Full-Time
    36000 - 60000 £ / year (est.)

    Application deadline: 2027-03-11

  • Z

    ZipRecruiter

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