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- Tasks: Craft data-driven solutions to combat fraud and enhance customer experience.
- Company: Join LexisNexis Risk Solutions, a leader in risk assessment.
- Benefits: Enjoy generous holidays, health perks, and a supportive work environment.
- Other info: Dynamic team culture with opportunities for personal and professional growth.
- Why this job: Make a real impact by protecting billions in revenue with innovative data science.
- 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. Ask them about their experiences and any tips they might have for landing the Data Scientist role. Personal connections can make a huge difference!
✨Tip Number 2
Prepare for the interview by brushing up on your SQL and Python skills. Be ready to discuss your past projects and how you've used machine learning to solve real-world problems. Show us your passion for data and fraud prevention!
✨Tip Number 3
Don’t forget to showcase your presentation skills! Practice explaining complex data insights in a simple way. We want to see how you can communicate effectively with both technical and non-technical audiences.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows us you’re genuinely interested in being part of our team at LexisNexis Risk Solutions.
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 directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at LexisNexis Risk Solutions
✨Know Your Data Science Stuff
Make sure you brush up on your data analysis, statistical modelling, and machine learning skills. Be ready to discuss specific projects where you've applied these techniques, especially in fraud detection or risk management. This will show that you can hit the ground running!
✨Master SQL and Python
Since proficiency in SQL and Python is crucial for this role, practice coding challenges and be prepared to demonstrate your skills during the interview. You might even get asked to solve a problem on the spot, so keep your coding environment handy!
✨Craft Your Story with Data
Prepare to present your past work in a way that highlights your attention to detail and ability to communicate insights effectively. Use visuals if possible, as this will help you convey complex information clearly, especially when discussing your experience with model development.
✨Stay Curious and Problem-Solving Mindset
Showcase your curiosity and problem-solving skills by discussing how you've tackled uncertainty in previous roles. Think of examples where you turned challenges into opportunities, particularly in fast-paced environments, as this aligns perfectly with what they’re looking for.