Role Description As an Analytics Team Lead, you will provide technical and project leadership across analytics engagements, using global data from the largest, real-time fraud detection platform to craft solutions for our enterprise customers and deliver against strategic projects. Your experience with data analysis, fraud, and technology will lead to immediate real-world impact in the form of lower customer friction, reduced fraud losses, and increased customer profitability. This role will involve a mix of internal collaboration and customer-facing work; the balance may vary based on business needs and project demand. Responsibilities - Lead analytics workstreams end-to-end: scope and plan deliverables, align stakeholders, and ensure delivery is on track. Provide day-to-day guidance and mentorship to analysts and partners (without direct people management responsibility initially). - Perform data analysis to identify both high-risk and trusted behaviours, translating findings into actionable insights ranging from intuitive rules to high-performing policy and score optimisations. You will design, test, and deploy single- and multi-product solutions on our Dynamic Decision Platform. - Stay ahead of fraud and industry trends and continuously propose new ways to fight emerging and dormant threats. Inspire the wider team to do the same and think outside the box beyond the data. - Produce materials summarising your analysis and present compelling narratives to customers and prospects. Guide your team to do the same through regular coaching, brainstorming, and maintaining best practice documentation. - Develop, standardize, and implement best practices and reusable assets (analytics playbooks, scripts, reporting/presentation templates, and repeatable processes) to scale analytics delivery across the National Cross Market Professional Services team. Provide enablement such as deck creation support and storytelling training to raise the bar on insight communication. - Collaborate with wider LNRS teams including Product, Engineering, Sales, and Marketing to help shape and improve our products and external messaging. Help quantify and champion the value and impact of analytics Professional Services engagements internally and externally. - Demonstrate a professional and customer-centric persona when interacting with internal stakeholders and, as needed, directly with customers via phone, e-mail, and chat. Qualifications - Proven analytics leadership in the security, fraud, financial crime, or payments industry—leading complex engagements end-to-end and influencing stakeholders without formal authority (experience in banking or other large enterprises preferred). - Strong interest in working with both large and small datasets; advanced SQL is required, and Python is preferred. - Demonstrated ability to choose and apply statistical/analytical techniques appropriately, with strong critical thinking and attention to detail (quantitative degree preferred). - Deep curiosity about fraud and financial crime patterns, including account takeover, scams and social engineering, Card Not Present (CNP) fraud, money laundering, mule activity, and synthetic identity. - Experience with fraud/risk decisioning platforms and operational analytics (e.g., ThreatMetrix, Featurespace, Hunter, iovation, BioCatch, Actimize/Falcon, etc.); experience delivering solutions in multi-product decisioning environments is a plus. - Polished communicator and presenter with the ability to remain calm and credible when ideas are challenged, maintaining a professional and customer-centric approach in internal and external interactions. Requirements - U.S. National Base Pay Range: $115,400 - $192,300. Geographic differentials may apply in some locations to better reflect local market rates. - If performed in Illinois, the base pay range is $121,200 - $201,900. - If performed in Chicago, IL, the base pay range is $126,900 - $211,500. - If performed in New York, the base pay range is $126,900 - $211,500. - If performed in New York City, the base pay range is $138,400 - $230,700. - If performed in Rochester, NY, the base pay range is $115,400 - $192,300. - If performed in Ohio, the base pay range is $109,500 - $182,700. - This job is eligible for an annual incentive bonus. Benefits - We are delighted to offer country-specific benefits. Click here to access benefits specific to your location. Company Description 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. 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.

Role Description As an Analytics Team Lead, you will provide technical and project leadership across analytics engagements, using global data from the largest, real-time fraud detection platform to craft solutions for our enterprise customers and deliver against strategic projects. Your experience with data analysis, fraud and technology will lead to immediate real-world impact in the form of lower customer friction, reduced fraud losses and as a result, increased customer profitability. This role will involve a mix of internal collaboration and customer-facing work; the balance may vary based on business needs and project demand. Responsibilities - Lead analytics workstreams end-to-end: scope and plan deliverables, align stakeholders, and ensure delivery is on track. Provide day-to-day guidance and mentorship to analysts and partners (without direct people management responsibility initially). - Perform data analysis to identify both high-risk and trusted behaviours, translating findings into actionable insights ranging from intuitive rules to high-performing policy and score optimisations. You will design, test, and deploy single- and multi-product solutions on our Dynamic Decision Platform. - Stay ahead of fraud and industry trends and continuously propose new ways to fight emerging and dormant threats. Inspire the wider team to do the same and think outside the box beyond the data. - Produce materials summarising your analysis and present compelling narratives to customers and prospects. Guide your team to do the same through regular coaching, brainstorming, and maintaining best practice documentation. - Develop, standardize, and implement best practices and reusable assets (analytics playbooks, scripts, reporting/presentation templates, and repeatable processes) to scale analytics delivery across the National Cross Market Professional Services team. Provide enablement such as deck creation support and storytelling training to raise the bar on insight communication. - Collaborate with wider LNRS teams including Product, Engineering, Sales and Marketing to help shape and improve our products and external messaging. Help quantify and champion the value and impact of analytics Professional Services engagements internally and externally. - Demonstrate a professional and customer-centric persona when interacting with internal stakeholders and, as needed, directly with customers via phone, e-mail, and chat. Qualifications - Proven analytics leadership in the security, fraud, financial crime, or payments industry—leading complex engagements end-to-end and influencing stakeholders without formal authority (experience in banking or other large enterprises preferred). - Strong interest in working with both large and small datasets; advanced SQL is required, and Python is preferred. - Demonstrated ability to choose and apply statistical/analytical techniques appropriately, with strong critical thinking and attention to detail (quantitative degree preferred). - Deep curiosity about fraud and financial crime patterns, including account takeover, scams and social engineering, Card Not Present (CNP) fraud, money laundering, mule activity, and synthetic identity. - Experience with fraud/risk decisioning platforms and operational analytics (e.g., ThreatMetrix, Featurespace, Hunter, iovation, BioCatch, Actimize/Falcon, etc.); experience delivering solutions in multi-product decisioning environments is a plus. - Polished communicator and presenter with the ability to remain calm and credible when ideas are challenged, maintaining a professional and customer-centric approach in internal and external interactions. Requirements - U.S. National Base Pay Range: $115,400 - $192,300. Geographic differentials may apply in some locations to better reflect local market rates. - If performed in Illinois, the base pay range is $121,200 - $201,900. - If performed in Chicago, IL, the base pay range is $126,900 - $211,500. - If performed in New York, the base pay range is $126,900 - $211,500. - If performed in New York City, the base pay range is $138,400 - $230,700. - If performed in Rochester, NY, the base pay range is $115,400 - $192,300. - If performed in Ohio, the base pay range is $109,500 - $182,700. - This job is eligible for an annual incentive bonus. Benefits - We are delighted to offer country specific benefits. Click here to access benefits specific to your location.

• Design, build, and maintain enterprise ETL/ELT pipelines supporting clinical and operational health datasets across the Military Health System • Develop and optimize data ingestion, transformation, and delivery workflows using tools such as Talend, AWS Glue, or Apache Airflow • Implement automated data quality validation, schema enforcement, and governance practices across pipelines • Troubleshoot data processing issues, performance bottlenecks, and integration defects across environments • Develop reusable pipeline components and automation scripts to improve scalability and maintainability • Collaborate with platform and DevSecOps teams to integrate pipelines into CI/CD workflows and automated testing frameworks • Support data interoperability with enterprise healthcare data standards including FHIR, HL7, and OMOP/CDM

• Build and improve data models that enable faster, more reliable decision-making across Product, Risk, and Analytics • Reduce bottlenecks by delivering clean, scalable datasets analysts can trust and use independently • Support both roadmap initiatives and ad hoc needs to keep business teams moving without delays • Improve data quality and structure so new products and features can be analyzed and launched with confidence