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- Tasks: Develop and implement machine learning models to combat fraud and enhance customer experience.
- Company: Join LexisNexis Risk Solutions, a leader in risk assessment and fraud detection.
- Benefits: Enjoy generous holidays, health benefits, and extensive learning resources.
- Other info: Dynamic team environment with opportunities for personal and professional growth.
- Why this job: Make a real-world impact by protecting billions in revenue from fraud.
- Qualifications: Experience in data science, proficiency in Python and SQL, and strong analytical skills.
The predicted salary is between 50000 - 70000 £ 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.
Working for you:
We know that your wellbeing and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:
- Generous holiday allowance with the option to buy additional days.
- Health screening, eye care vouchers and private medical benefits.
- Wellbeing programs.
- Life Assurance.
- Access to a competitive contributory pension scheme.
- Save As You Earn share option scheme.
- Travel Season ticket loan.
- Electric Vehicle Scheme.
- Optional Dental Insurance.
- Maternity, paternity and shared parental leave.
- Employee Assistance Programme.
- Access to emergency care for both the elderly and children.
- RECARES days, giving you time to support the charities and causes that matter to you.
- Access to employee resource groups with dedicated time to volunteer.
- Access to extensive learning and development resources.
- Access to employee discounts scheme via Perks at Work.
Data Scientist employer: LexisNexis Risk Solutions
Contact Detail:
LexisNexis Risk Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Network like a pro! Reach out to current employees at LexisNexis Risk Solutions on LinkedIn. A friendly chat can give us insights into the company culture and maybe even a referral!
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your data science projects, especially those related to fraud detection or risk management. This will help us stand out during interviews.
✨Tip Number 3
Practice makes perfect! Brush up on your SQL and Python skills, and be ready to tackle some technical questions or coding challenges during the interview process.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist role. Highlight your experience with data analysis, machine learning, and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to tell us why you're the perfect fit for our team. Share your passion for data science and how you can contribute to tackling fraud and risk challenges. Keep it engaging and personal!
Showcase Your Technical Skills: Don’t forget to mention your proficiency in Python and SQL! If you have experience with BI tools or MLOps, make sure to include that too. We love seeing candidates who can hit the ground running with their technical expertise.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at LexisNexis Risk Solutions
✨Know Your Data Inside Out
Make sure you brush up on your data analysis skills, especially in SQL and Python. Be ready to discuss specific projects where you've implemented machine learning models or tackled fraud-related challenges. This will show that you not only understand the theory but can apply it in real-world scenarios.
✨Craft Your Story with Data
Prepare to present your past work in a way that highlights your attention to detail and storytelling ability. Use visuals if possible, as this can help convey complex information more clearly. Remember, the interviewers want to see how you can communicate insights effectively, especially to non-technical audiences.
✨Stay Curious and Problem-Solving Focused
Demonstrate your curiosity by discussing how you've approached uncertainty in previous roles. Share examples of how you've transformed challenges into opportunities, particularly in the context of fraud detection or risk management. This will showcase your proactive mindset and adaptability.
✨Be Ready for Fast-Paced Questions
Since the role involves multi-tasking and prioritisation, prepare for questions that assess how you handle changing priorities. Think of examples from your experience where you successfully managed multiple tasks under tight deadlines. This will illustrate your ability to thrive in a dynamic environment.