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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 top industry experts.
- Qualifications: Experience in data science, proficiency in Python and SQL required.
- Other info: Work in a fast-paced environment tackling global fraud challenges.
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 solutions in the industry will help you engage in meaningful conversations during interviews and demonstrate your passion for the field.
✨Tip Number 2
Network with professionals in the data science and fraud prevention sectors. Attend relevant meetups, webinars, or conferences to connect with industry experts and gain insights that could give you an edge when applying for the Graduate Data Scientist position.
✨Tip Number 3
Brush up on your SQL and Python skills by working on personal projects or contributing to open-source initiatives. Being able to showcase your practical experience with these tools can significantly enhance your candidacy.
✨Tip Number 4
Prepare to discuss your problem-solving approach in detail. Be ready to share examples of how you've tackled complex data challenges in the past, as this will highlight your analytical thinking and ability to thrive in a fast-paced environment.
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, statistical modelling, and machine learning. Emphasise any previous roles related to fraud, risk, or payments, and showcase your proficiency in Python and SQL.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your skills align with the responsibilities outlined in the job description, particularly your experience with machine learning and your ability to communicate insights effectively.
Showcase Your Projects: If you have worked on relevant projects, especially those involving fraud detection or risk assessment, include them in your application. Describe your role, the technologies used, and the impact of your work to demonstrate your hands-on experience.
Prepare for Technical Questions: Be ready to discuss your technical skills in detail, particularly your experience with SQL and Python. Prepare to explain your approach to developing machine learning models and how you would apply them to real-world fraud scenarios.
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 model development.
✨Demonstrate Problem-Solving Abilities
Prepare examples that showcase your curiosity and problem-solving skills. Discuss how you've transformed uncertainty into actionable insights, particularly in the context of 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.
