The posting
This position leads a team within Applied AI, delivering AI/ML solutions into business workflows with clear business value. The role is accountable for a portfolio of work across business areas, assessing foundational and ideological needs for delivering AI at scale, the technical quality and operational readiness of delivered solutions, and the work environment to support and grow the people who deliver them.
The Senior Manager combines people and portfolio leadership with current technical depth, contributing directly to early proofs of concept and exploratory analysis while providing architecture guidance, design review, and code review throughout delivery. The role works alongside peer data science leaders and partners across business, platform, data, architecture, security, governance, risk, and operations teams. The role also manages external engagements, including those with consultants, vendors, and academic researchers.
Position Compensation Range:
$150,000.00 - $255,000.00
Pay Rate Type:
Salary
Compensation may vary based on the job level and your geographic work location. Relocation support is offered for eligible candidates.
Primary Accountabilities
Lead AI solution delivery
- Manage the development and delivery of applied AI, machine learning, and advanced analytics solutions that address high-value business problems.
- Proactively offer new advancements in technology or emerging start-up capabilities to drive more comprehensive solutions with a competitive edge.
- Own team-level delivery commitments, sequencing, dependencies, risks, and quality while keeping work aligned to enterprise priorities.
- Ensure solutions are production-oriented, maintainable, measurable, supportable, and designed for responsible use.
Manage portfolio demand and execution
- Partner with the Applied AI Director, peer leaders, and business stakeholders to shape, prioritize, and sequence a cross-domain portfolio under capacity and dependency constraints by both leveraging existing solutions and finding commonalities for shared capabilities.
- Translate ambiguous needs into clear problem statements, delivery plans, decision points, and measurable outcomes.
Provide hands-on technical leadership
- Hone technical skills and contribute to early proofs of concept, exploratory analysis, solution framing, merge request review, or other situations where hands-on leadership will accelerate learning.
- Guide architecture and design decisions, review code and analytical work, and ensure technical tradeoffs are appropriate to the problem.
- Set evidence-based criteria for moving work from exploration to proof of concept, pilot, production, scale, or closure.
Drive value realization, adoption, and operational readiness
- Define success measures with business and product owners and connect technical performance to business outcomes.
- Ensure delivered capabilities have clear ownership, adoption plans, support models, escalation paths, monitoring expectations, and continuous improvement mechanisms.
Embed responsible AI and evaluation discipline
- Embed responsible AI practices into applied AI delivery in alignment with enterprise governance expectations.
- Partner with business, governance, risk, security, privacy, legal, compliance, and platform teams to define domain-specific evaluation criteria for quality, accuracy, fairness, safety, compliance, customer experience, and business usefulness.
Advance process-level AI transformation
- Partner with business and technology leaders to identify, assess, and redesign high-value workflows.
- Help teams connect individual AI capabilities into coherent business process journeys rather than isolated point solutions.
- Clarify the changes required to human-driven processes, data, technology, and adoption to realize customer-centered value.
Develop reusable AI solution patterns
- Identify repeated solution patterns across domains and partner with AI Platform Engineering to convert them into reusable frameworks, components, and delivery accelerators.
- Reduce one-off builds where shared approaches can improve speed, quality, governance, observability, and long-term supportability.
Lead people and develop talent
- Build and lead a high-performing team of data scientists and applied AI practitioners with the skills and capacity required by the enterprise portfolio, reflective of achieving the enterprise strategic goals.
- Set clear priorities and expectations, coach individual contributors, develop technical and leadership capability, and communicate strategic vision.
- Create a collaborative, accountable, performance-driven, and inclusive environment.
Lead external delivery partnerships
- Manage vendor, consulting, contract staffing, academic, and research engagements that support Applied AI delivery.
- Define scope, outcomes, quality expectations, knowledge-transfer requirements, hand-off criteria, and ownership after a pilot or engagement ends.
- Hold partners accountable for responsible delivery, while ensuring internal teams retain sufficient understanding and control of critical solutions.
Communicate with clarity and influence
- Move between business problem, technical solution, delivery trade-offs, and business outcomes; quantify impact in terms stakeholders already use.
- Communicate complex technical concepts, uncertainty, risks, and recommendations to executive, business, technical, and governance audiences.
- Form clear points of view, invite challenge, change position when evidence supports it, and drive decisions across organizational boundaries.
Specialized Knowledge & Skills Requirements
- Demonstrated experience leading data scientists, machine learning practitioners, or applied AI specialists, including coaching, performance leadership, and talent development.
- Demonstrated experience delivering machine learning, AI, or advanced analytics solutions into production business workflows with measurable outcomes.
- Current hands-on technical ability, including the ability to prototype independently, read and review code, assess analytical work, and lead architecture and design conversations.
- Strong understanding of AI system design, including retrieval patterns, structured output, agentic workflows, evaluation methods for non-deterministic systems, observability, and production risk.
- Demonstrated ability to originate work from enterprise goals and manage a delivery portfolio under constraint, including prioritizing, sequencing, resizing, deferring, or declining work.
- Experience running exploration deliberately with stated evidence and criteria for progressing from proof of concept to pilot and production.
- Experience establishing operational ownership, adoption measures, support expectations, and continuous improvement practices for production solutions.
- Experience managing vendor, consulting, academic, research, or contract staffing engagements, including scope, quality, knowledge transfer, handoff criteria, and ownership after an engagement ends.
- Demonstrated experience partnering across Information Security, Enterprise Architecture, Governance, Risk, Legal, Privacy, Compliance, Product, Operations, Data Engineering, and Application Development.
- Demonstrated ability to communicate complex technical concepts, uncertainty, tradeoffs, and recommendations to executive audiences and influence without formal authority.
- Working knowledge of insurance, risk, or a comparably regulated domain, and demonstrated ability to lead through ambiguity, emerging technology, growth, and organizational change.
Key Interfaces and Partners
- Director, Applied AI, and peer data science leaders within Applied AI for portfolio alignment, solution standards, talent strategy, shared delivery, and escalation.
- Strategy, product, program, and portfolio leaders for enterprise goals, demand shaping, prioritization, operating model alignment, delivery orchestration, and value realization.
- Business partners in Claims, Sales, Service & Marketing, and Product/Underwriting, including business product owners, domain experts, operations leaders, and end users.
- AI Platform Engineering for MLOps capabilities, deployment paths, reusable frameworks, observability, platform standards, production support patterns, and L3 enhancement partnership.
- BI Engineering & Enablement and Data Engineering for shared metrics, semantic context, data pipelines, curated data, and AI-ready data assets.
- Enterprise Architecture, Information Security, Infrastructure, Cloud, Application Development, and Digital Services for architecture alignment, security review, integration, hosting, deployment, reliability, and operational support.
- AI Governance, Data Governance, Model Risk, Privacy, Legal, Risk, and Compliance for responsible AI requirements, control design, evaluation expectations, approvals, and ongoing oversight.
- External vendor, consulting, contract staffing, academic, and research partners for specialized expertise, delivery capacity, innovation, and knowledge transfer.
Additional Information
- To ensure a strong start, all employees participate in our New Employee Orientation during their first week. This experience is held in person at our Madison, WI Headquarters or one of our AmFam core locations to help you connect with our mission, meet key team members and build relationships that support your growth. At times, sessions may be delivered virtually based on scheduling and availability.
- Offer to selected candidate will be made contingent on the results of applicable background checks
- Offer to selected candidate is contingent on signing a non-disclosure agreement for proprietary information, trade secrets, and inventions
- Sponsorship will not be considered for this position unless specified in the posting
In this hybrid role, you will be expected to work a minimum of 10 days per month from the office. Candidates should reside within approximately 35-50 miles of one of the following office locations: Madison, WI 53783 or Boston, MA 02110.
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We provide benefits that support your physical, emotional, and financial wellbeing. You will have access to comprehensive medical, dental, vision and wellbeing benefits that enable you to take care of your health. We also offer a competitive 401(k) contribution, a pension plan, an annual incentive, 9 paid holidays and a paid time off program (23 days accrued annually for full-time employees). In addition, our student loan repayment program and paid-family leave are available to support our employees and their families. Interns and contingent workers are not eligible for American Family Insurance Group benefits.
We are an equal opportunity employer. It is our policy to comply with all applicable federal, state and local laws pertaining to non-discrimination, non-harassment and equal opportunity. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.
American Family Insurance is committed to the full inclusion of all qualified individuals. If a reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please email [email protected] to request a reasonable accommodation.
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