• Help define, launch and drive value-based pricing go-to-market initiatives • Analyze competitive pricing and revenue model strategies • Develop financial scenario models and propose pricing options • Manage monthly pricing reporting insights and trends • Design and execute pricing tests and promotional pricing across the product portfolio • Create pricing enablement content to educate customers and sales on Tenable licensing

• Support the execution and management of pricing and rebate programs and processes, ensuring alignment with organizational goals. • Conduct analysis and data management activities to support pricing strategies, utilizing SAS/SQL as needed. • Collaborate with cross-functional teams to ensure seamless integration and execution of pricing initiatives. • Monitor and troubleshoot issues within the pricing and rebate processes, conducting research to identify root causes and implement effective solutions. • Maintain ongoing communication with teams supporting the overall pricing program, ensuring all parties are informed and aligned. • Develop and maintain documentation related to pricing processes, program management, and issue resolution. • Provide insights and recommendations based on data analysis to enhance pricing strategies and improve overall program performance.

Role Description As a team member in Finance at Nationwide, a Fortune 100 company with nearly $70 billion in annual sales, the opportunities are endless! Let Nationwide help create your career journey! At Nationwide®, “on your side” goes beyond just words. Our customers are at the center of everything we do and we’re looking for associates who are passionate about delivering extraordinary care. Nationwide’s Commercial Lines team is hiring a Predictive Modeler / Pricing Modeler to help shape sophisticated insurance pricing models and deliver insights that influence business decisions. This role is ideal for someone who brings strong technical modeling skills and either a solid actuarial foundation, commercial insurance knowledge, or both. In this position, you will work at the intersection of predictive analytics, actuarial thinking, and pricing strategy. You will partner closely with teammates and business partners to build, enhance, and refine models that support commercial insurance pricing. You will also have the opportunity to work across a variety of projects, expand your technical toolkit, and deepen your understanding of how modeling drives real business outcomes. What makes this opportunity stand out: - A unique blend of predictive modeling, pricing, and commercial actuarial work - Highly visible projects that support important pricing decisions - The chance to work with R, Python, SQL, and modern modeling techniques in a business-critical environment - Exposure to insurance data, actuarial mechanics, and the nuances of commercial lines pricing - A collaborative team structure where modeling, actuarial, and business expertise come together - Broad skill development across predictive analytics, actuarial concepts, and cross-functional partnership - The opportunity to contribute quickly if you already understand insurance, actuarial modeling, or pricing workflows We are especially interested in candidates who can combine technical modeling ability with practical business application. Strong candidates may come from actuarial, predictive modeling, pricing analytics, or data science backgrounds, but they should be excited by insurance use cases and motivated to work in a role where actuarial and modeling foundations both matter. The strongest profiles will typically offer a mix of the following: - Predictive modeling or pricing model experience - Commercial insurance, property and casualty, or broader insurance analytics experience - Knowledge of actuarial concepts, pricing mechanics, or loss cost / relativity-based thinking - Hands-on coding experience in R, Python, SQL, or similar analytical tools - The ability to translate data into actionable pricing or underwriting insight - Strong problem-solving skills and the ability to work through technical detail in a collaborative setting This is not a pure data science role removed from the business, and it is not a traditional actuarial role in the narrow sense. It is a hybrid opportunity for someone who wants to apply predictive modeling in a commercial actuarial and insurance pricing environment, learn from strong partners, and grow into an even more versatile analytics professional. If you want to build models that matter, strengthen your pricing and actuarial expertise, and work on meaningful commercial insurance challenges, this role offers the chance to do exactly that. This role can be staffed remote. Qualifications - Undergraduate studies in finance, accounting, economics, statistics, mathematics or related subject area required. - Graduate-level studies in a related field with advanced degree highly desirable. - Progress toward FCAS, FSA, CQF, CFA or similar preferred. - Typically, five or more years of related work experience in financial risk modeling or actuarial functions. Requirements - Working knowledge of machine learning, stochastic processes, Monte Carlo simulations, sampling methods and other statistical techniques applicable to specialized risk modeling. - Prefer mathematical knowledge of specialized risk models such as those used in hedging, economic scenario generation, catastrophe, credit risk, etc. - Solid understanding of risk management operations such as asset-liability management, portfolio risk assessment, hedging, etc. - Proficient written and verbal communication skills. - Intermediate proficiency with Excel and common statistical software such as R, SAS, Python, or MATLAB. Benefits - Medical/dental/vision - Life insurance, short and long term disability coverage - Paid time off with newly hired associates receiving a minimum of 18 days paid time off each full calendar year pro-rated quarterly based on hire date - Nine paid holidays - 8 hours of Lifetime paid time off - 8 hours of Unity Day paid time off - 401(k) with company match - Company-paid pension plan - Business casual attire

• Set and own company-wide pricing strategy for products and services: margin targets, price positioning, discount governance, and the deal review framework that enforces them • Own deal-level margin improvement as a managed, measurable target: track bookings margin versus guidance floor by product family, identify where margin is leaking and why, and drive systematic improvement quarter over quarter • Define pricing guardrails and exception thresholds; establish clear decision rights for rep-level approvals, deal desk reviews, and escalations to sales leadership or Finance • Partner with Finance on margin target setting and profitability review cadences; ensure pricing strategy reflects the economics of the business, not just competitive positioning • Work directly with the SVP of Revenue Operations and regional sales VPs to drive pricing attainment as a managed behavior across the selling organization • Own the pricing guidance algorithm: understand the clustering methodology, product family taxonomy, and deal categorization logic well enough to govern updates, explain the output to sales leadership, and defend recommendations under scrutiny • Approve updates to the guidance algorithm based on Technical Pricing Analyst recommendations; partner with the GTM Tooling team on execution of approved changes in the tool • Drive deal-guidance adherence from a low baseline toward an 80 percent-plus target; treat exception rates as diagnostic data, not acceptable noise • Own the Pricing Guidance tool pilot: define success criteria, coordinate the seller pilot group, review exception reports from day one, and lead the post-pilot retrospective that shapes the scale rollout • Oversee deal intelligence operations: the team of Deal Intelligence Analysts who provide pricing consultative support to the frontline, assist GMs with deal review decisions, and provide guidance based on prior approval cycles • Set deal review thresholds and response times; define what requires review, who owns it, and what the SLA is — with a target deal-desk cycle time of less than 24 hours • Build the deal review as a learning system: every exception, every approval, and every loss contains signal about where the pricing framework needs adjustment • Lead the Technical Pricing Analyst who processes and synthesizes margin improvement, win/loss, and deal velocity analytics and recommends adjustments to the guidance algorithm • Own the analytical foundation the pricing function runs on: product family tagging accuracy, deal categorization completeness, and the data quality standards that make the guidance output trustworthy • Translate analytics outputs into pricing strategy recommendations the selling organization can act on; the output of this function is a decision, not a report • Build and lead the Pricing and Deal Intelligence team: Technical Pricing Analyst (1) and Deal Intelligence Analysts (3); hire the team, set performance standards, and develop their capabilities over time • Establish the operating standard for the pricing function: what a deal review looks like, how exception rates are tracked and reported, how algorithm updates are proposed and approved, and what the weekly cadence with sales leadership looks like • Build a pricing function that sellers experience as a deal partner that makes them more competitive, not a compliance function that slows them down.