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- Tasks: Analyse data and develop machine learning models to combat fraud.
- Company: Join LexisNexis Risk Solutions, a leader in risk assessment and fraud prevention.
- Benefits: Enjoy flexible working options and opportunities for professional growth.
- Why this job: Make a real-world impact while collaborating with innovative teams in a fast-paced environment.
- 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 to protect billions in revenue.
The predicted salary is between 28800 - 48000 £ per year.
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, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management.
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.
- Applying 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.
Graduate Data Scientist - Fraud employer: LexisNexis Risk Solutions
Contact Detail:
LexisNexis Risk Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Graduate Data Scientist - Fraud
✨Tip Number 1
Familiarise yourself with the latest trends in fraud detection and prevention. Understanding current challenges and innovations in the field will not only help you during interviews but also demonstrate your genuine interest in the role.
✨Tip Number 2
Network with professionals in the data science and fraud prevention sectors. Attend relevant meetups or webinars, and connect with people on LinkedIn to gain insights and potentially get referrals for the position.
✨Tip Number 3
Brush up on your SQL and Python skills by working on real-world projects or contributing to open-source initiatives. This hands-on experience will not only enhance your technical abilities but also provide you with concrete examples to discuss during interviews.
✨Tip Number 4
Prepare to showcase your problem-solving skills through case studies or practical scenarios related to fraud detection. Being able to articulate your thought process and approach to tackling complex issues will set you apart from other candidates.
We think you need these skills to ace Graduate Data Scientist - Fraud
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data analysis, machine learning, and any specific projects related to fraud detection. Use keywords from the job description to align your skills with what LexisNexis Risk Solutions is looking for.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data science and fraud prevention. Mention specific experiences that demonstrate your problem-solving skills and ability to work in fast-paced environments, as well as your proficiency in Python and SQL.
Showcase Your Projects: If you have worked on relevant projects, especially those involving machine learning or fraud detection, include them in your application. Briefly describe the challenges you faced, the solutions you implemented, and the impact of your work.
Prepare for Technical Questions: Be ready to discuss your technical skills in detail, particularly your experience with SQL and Python. You may be asked to solve problems or explain your approach to developing machine learning models, so brush up on these topics before the interview.
How to prepare for a job interview at LexisNexis Risk Solutions
✨Showcase Your Technical Skills
Make sure to highlight your proficiency in Python and SQL during the interview. Be prepared to discuss specific projects where you've applied these skills, especially in data analysis or machine learning contexts.
✨Demonstrate Problem-Solving Abilities
Prepare examples that showcase your curiosity and problem-solving skills. Think of situations where you transformed uncertainty into actionable insights, particularly in fraud detection or risk management.
✨Communicate Effectively
Practice delivering clear and polished presentations. You may be asked to explain complex data findings, so focus on how you can communicate insights effectively to both technical and non-technical audiences.
✨Understand the Industry
Familiarise yourself with current trends in cybercrime and fraud prevention. Being knowledgeable about account takeover, scams, and other relevant topics will demonstrate your commitment and understanding of the field.
