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For Companies At a Glance
- Tasks: Lead product execution and manage a portfolio of machine learning-driven products.
- Company: Join LexisNexis Risk Solutions, a leader in risk assessment and innovative solutions.
- Benefits: Enjoy a diverse workplace with inclusive leadership and opportunities for growth.
- Why this job: Be part of a dynamic team driving impactful solutions in digital identity and fraud prevention.
- Qualifications: 5+ years in product management, expertise in Machine Learning, strong analytical skills required.
- Other info: We value diversity and encourage applications from all backgrounds.
The predicted salary is between 43200 - 72000 £ per year.
Senior ML Product Manager
Are you an expert in Machine Learning?
Will you like to be part of a dynamic global team?
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. You can learn more about LexisNexis Risk at the link below:
About the Role
As a Senior Product Manager, you will be responsible for managing a portfolio of machine learning driven digital identity and device identification products alongside being a product owner of a team of engineers that directly work on delivering innovative functionality and improving existing features. You will evangelize the value and vision of your areas of focus while promoting a culture of data-driven decision making and risk management with customers and customer-facing teams within our organization. You will be responsible for advancing our industry-leading digital and device identification products by leading product execution, creating product development plans, managing backlogs, and working closely with a cross-functional team developing and maintaining product ideas that solve customer problems.
Responsibilities
- Leading product execution, development plans, and backlog management.
- Working closely with cross-functional teams to develop and maintain product ideas that solve customer problems.
- Identifying opportunities to improve our digital and device identifiers and insights.
- Driving experimentation, optimization, and measuring outcomes using best practices.
- Managing go-to-market strategies, ensuring product features and roadmaps are clearly communicated to all stakeholders.
Requirements
- At least 5+ years of experience in product management in software.
- Expertise in Machine Learning.
- Fraud prevention knowledge preferred but not required.
- Digital identity or device profiling knowledge preferred but not required.
- Analytical skills needed to drive insight from billions of data points using tools such as SQL and Snowflake.
- Demonstrate the ability to solve complex problems with simple solutions.
- Strong communication skills and confidence to act as an evangelist for digital and device identification within the business.
- Track record of communicating complex technical concepts to a non-technical business audience.
- Track record of converting non-technical business requirements into technical design documents.
- Demonstrated ability to manage multiple tasks, projects, and priorities.
- Strong understanding of software development methodologies like Agile Scrum, Kanban, writing user stories, and some background in software development.
- Bachelor's or Master's degree in a technical subject is preferred.
At LexisNexis Risk Solutions, having diverse employees with different perspectives is key to creating innovative new products for our global customers. We have 30 diversity employee networks globally and prioritize inclusive leadership and equitable processes as part of our culture. Our aim is for every employee to be the best version of themselves. We actively welcome applications from candidates of diverse backgrounds and underrepresented groups.
We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form: , or please contact 1-855-833-5120.
Please read our Candidate Privacy Policy .
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Senior ML Product Manager (London) employer: Lexis Nexis
Contact Detail:
Lexis Nexis Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Product Manager (London)
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning and digital identity solutions. Being able to discuss recent advancements or case studies during your interview can demonstrate your passion and expertise in the field.
✨Tip Number 2
Network with professionals in the industry, especially those who work at LexisNexis or similar companies. Engaging with them on platforms like LinkedIn can provide insights into the company culture and expectations for the role.
✨Tip Number 3
Prepare to showcase your experience with Agile methodologies and product management tools. Be ready to discuss specific projects where you successfully managed backlogs and collaborated with cross-functional teams.
✨Tip Number 4
Highlight your ability to communicate complex technical concepts to non-technical audiences. Practising how you would explain your previous projects can help you articulate your value during interviews.
We think you need these skills to ace Senior ML Product Manager (London)
Some tips for your application 🫡
Understand the Role: Before applying, make sure to thoroughly understand the responsibilities and requirements of the Senior ML Product Manager position. Tailor your application to highlight your relevant experience in product management and machine learning.
Highlight Relevant Experience: In your CV and cover letter, emphasise your 5+ years of experience in product management, particularly any work related to machine learning, digital identity, or device profiling. Use specific examples to demonstrate your expertise.
Showcase Communication Skills: Since strong communication skills are essential for this role, ensure your application reflects your ability to convey complex technical concepts clearly. Consider including examples of how you've successfully communicated with non-technical stakeholders.
Demonstrate Problem-Solving Abilities: Provide examples in your application that showcase your analytical skills and your ability to solve complex problems with simple solutions. This will help illustrate your fit for the role and your understanding of the challenges faced in product management.
How to prepare for a job interview at Lexis Nexis
✨Showcase Your Machine Learning Expertise
Be prepared to discuss your experience with machine learning in detail. Highlight specific projects where you've successfully implemented ML solutions, and be ready to explain the impact of these projects on business outcomes.
✨Communicate Complex Concepts Simply
Since you'll need to convey technical information to non-technical stakeholders, practice explaining complex machine learning concepts in simple terms. Use analogies or real-world examples to make your points clearer.
✨Demonstrate Your Product Management Skills
Prepare to discuss your experience with product management methodologies like Agile and Scrum. Be ready to share examples of how you've managed backlogs, prioritised tasks, and collaborated with cross-functional teams to deliver successful products.
✨Emphasise Your Analytical Skills
Given the role's focus on data-driven decision making, be ready to discuss how you've used analytical tools like SQL or Snowflake to derive insights from data. Share specific instances where your analysis led to improved product features or customer satisfaction.
