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About This Role
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 is seeking a forward-thinking technology leader to drive enterprise-scale data, analytics, cloud modernization, and artificial intelligence solutions across Nationwide Financial’ s Actuarial organization. This role is ideal for a hands-on data and technology strategist who can translate business priorities into scalable, secure, cloud-native solutions and help actuarial teams adopt modern data and AI-enabled ways of working.
Reporting to the Senior Technical Director, you will serve as a trusted advisor and primary liaison between actuarial and business stakeholders and technology teams. You will identify high-value opportunities, lead discovery and solution-design discussions, shape technology roadmaps, and guide delivery across data, platform engineering, DevOps, security, governance, architecture, and application development teams. This role will also provide technical leadership and mentoring to others.
What You’ll Do
- Partner with actuarial leaders, business stakeholders, and digital teams to prioritize and deliver data, analytics, automation, and technology opportunities.
- Modernize legacy processes and applications into cloud-native, data-driven, and AI-enabled solutions.
- Design and govern modern data platforms using technologies such as Snowflake, Databricks, AWS, data lakes, lakehouse architectures, and open table formats.
- Establish practical standards for data quality, metadata, lineage, governance, security, access controls, and data observability.
- Identify responsible opportunities to apply generative AI, machine learning, analytics, and automation to improve productivity, decision-making, and operational efficiency.
- Lead discovery sessions, capability assessments, architecture reviews, solution-design workshops, and technology evaluations.
- Recommend improvements to platform scalability, resilience, performance, and cost optimization.
- Communicate complex technical concepts clearly to both executive and technical audiences and influence strategic technology investments.
What You’ll Bring
- Bachelor’s degree in computer science, engineering, information systems, or a related technical discipline highly preferred.
- 10+ years of hands-on experience in data architecture, data engineering, analytics, and enterprise solution architecture.
- 5+ years designing and implementing cloud-native modern data platforms using Snowflake, Databricks, and AWS.
- 3+ years delivering AI, machine learning, generative AI, or agentic AI solutions into production environments.
- Proven experience leading enterprise-scale digital transformation, data modernization, and AI adoption initiatives.
- Strong expertise in modern data platform architecture, lakehouse architectures, and cloud-native design principles.
- Demonstrated success partnering directly with business stakeholders to develop technology roadmaps and solution strategies.
- Strong architectural thinking, executive communication, strategic problem-solving, and the ability to influence without direct authority.
Additional Experience That’s Valuable
- Python, SQL, PySpark, API integration, CI/CD, DevOps, and infrastructure as code.
- Experience applying AI, analytics, or automation to business processes with appropriate governance and controls.
- Snowflake, Databricks, machine learning, or generative AI certifications.
- Experience with data lakes, medallion architecture, Parquet, Apache Iceberg, Delta Lake, data interoperability, metadata management, data lineage, data governance, and observability.
- Insurance or actuarial experience, or a strong interest in learning actuarial business processes and translating them into technology solutions.
- Experience working with Enterprise Data Office, Infrastructure, platform engineering, security, governance, or architecture organizations.
Why this opportunity This is an opportunity to shape how a major financial services organization uses data, analytics, cloud platforms, and AI to improve actuarial processes and business outcomes. You’ll have meaningful enterprise scope, visibility with business and technology leaders, and the opportunity to help move teams from traditional processes to modern, governed, AI-enabled operating models.
If you are a data architecture, cloud modernization, enterprise technology, or AI leader who enjoys solving complex business problems and helping teams transform, we invite you to apply.
Location: Columbus, Ohio OR fully remote
Job Description Summary Our Nationwide Financial (NF) Actuarial team members have opportunities to use state-of-the-art tools, are encouraged to innovate and learn to master actuarial methodologies. If this sounds like a place you could thrive, then we want to know more about you!
As a NF Technical Director, you’ll show a mastery of business operations, systems, and actuarial methodologies. You will provide thought leadership for the performance of complex actuarial techniques and procedures and risk analysis. You’ll develop a strategic vision to ensure efficient, consistent and coordinated data and analysis. Additionally, you’ll provide business recommendations that are essential to success and the protection we provide our customers.
Job Description Key Responsibilities:
- Crafts direction for the compilation, categorization and analysis of data.
- Constructs sophisticated testing tools and completes analysis of product calculations.
- Designs and maintains actuarial, projection and valuation models.
- Conducts or delegates financial reporting and analysis.
- Leads, consults and builds strategies for pricing and implementation of products.
- Completes risk analysis specific lines of business.
- Leads efforts to establish reserves and other valuation items under multiple accounting regimes (statutory, GAAP, and tax) for products.
- Builds strategic direction and provides analysis and pricing of reinsurance.
- Develops and implements effective communication strategies and recommendations to leadership.
- Completes activities related to a niche phase of data and analytics such as data governance, data strategy development, data modeling, data requirements, data manipulation and/or requirement elicitation.
- Leads sophisticated business processes, new programs/modifications of existing applications and/or cross-functional projects/teams.
May Perform Other Responsibilities As Assigned. Reporting Relationships: Reports to Actuarial leader.
Typical Skills And Experiences Education: Undergraduate degree in actuarial science, mathematics, statistics, computer science, or similar background requiring modeling, programming or quantitative analysis.
Experience: Typically, eight or more years of actuarial or similar experience.
Knowledge, Abilities and Skills: Proven knowledge of actuarial business functions, statistical methods of analysis, company actuarial systems and databases, and their integration, and technical facility with leading computer tools and technology. Mastery of Microsoft Office products (including VBA within Access and Excel), programming languages and actuarial models. Basic level of organizational agility and issue resolution. Proficient level of command skills, strategic agility, delegation, inspiring others, business perspective, and accounting. Mastery level of perspective, problem solving, customer focus, dealing with ambiguity, drive for results, analysis, learning on the fly, and making the best use of system capabilities.
Other criteria, including leadership skills, competencies and experiences may take precedence.
Staffing exceptions to the above must be approved by the hiring manager’s leader and HR Business Partner.
Values: Regularly and consistently demonstrates Nationwide Values.
Job Conditions Overtime Eligibility: Not Eligible (Exempt)
Working Conditions: Normal office environment.
ADA: The above statements cover what are generally believed to be principal and essential functions of this job. Specific circumstances may allow or require some people assigned to the job to perform a somewhat different combination of duties.
We currently anticipate accepting applications until 11/14/2026. However, we encourage early submissions, as the posting may close sooner if a strong candidate slate is identified before the deadline.
Benefits We have an array of benefits to fit your needs, including: 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, and more. To learn more about the benefits we offer, click here.
Nationwide is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive culture where everyone feels challenged, appreciated, respected and engaged. Nationwide prohibits discrimination and harassment and affords equal employment opportunities to employees and applicants without regard to any characteristic (or classification) protected by applicable law.
This position could be filled within any of the lower 48 U.S. states.
#actuarial
Smoke-Free Iowa Statement: Nationwide Mutual Insurance Company, its affiliates and subsidiaries comply with the Iowa Smokefree Air Act. Smoking is prohibited in all enclosed areas on or around company premises as well as company issued vehicles. The company offers designated smoking areas in which smoking is permitted at each individual location. The Act prohibits retaliation for reporting complaints or violations. For more information on the Iowa Smokefree Air Act, individuals may contact the Smokefree Air Act Helpline at 888-944-2247.
For NY residents please review the following state law information: Notice of Employee Rights, Protections, and Obligations LS740 (ny.gov) https://dol.ny.gov/system/files/documents/2022/02/ls740_1.pdf
Note To Employment Agencies We value the partnerships we have built with our preferred vendors. Nationwide does not accept unsolicited resumes from employment agencies. All resumes submitted by employment agencies directly to any Nationwide employee or hiring manager in any form without a signed Nationwide Client Services Agreement on file and search engagement for that position will be deemed unsolicited in nature. No fee will be paid in the event the candidate is subsequently hired as a result of the referral or through other means.
Nationwide pays on a geographic-specific salary structure and placement within the actual starting salary range for this position will be determined by a number of factors including the skills, education, training, credentials and experience of the candidate; the scope, complexity and location of the role as well as the cost of labor in the market; and other conditions of employment. If a Sales job, Sales Incentives, based on performance goals are possible in addition to this range. Note on Compensation for Part-Time Roles: Please be aware that the salary ranges listed below reflect full-time compensation. Actual compensation may be prorated based on the number of hours worked relative to a full-time schedule.
The national salary range for Financial Technical Director - Actuarial : $122,000.00-$214,000.00
The expected starting salary range for Financial Technical Director - Actuarial : $122,000.00 - $182,000.00
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Nationwide, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Nationwide AI Hiring
Nationwide has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Columbus, OH.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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