Applied AI Senior Manager

American Family Insurance•Boston, MA

7d•$150,000 - $255,000•Hybrid

About The Position

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 organizational needs for delivering AI at scale, ensuring the technical quality and operational readiness of solutions, and fostering an environment that supports and develops the people delivering them. The Senior Manager combines people and portfolio leadership with current technical depth. They contribute directly to early proofs of concept and exploratory analysis while providing architecture guidance, design review, and code review throughout delivery. The role works with peer data science leaders and partners across business, platform, data, architecture, security, governance, risk, and operations teams, and manages external engagements with consultants, vendors, and academic researchers. The role leads AI solution delivery and portfolio execution; provides hands-on technical leadership; drives value realization, adoption, and operational readiness; embeds responsible AI and evaluation practices; advances process-level AI transformation; develops reusable AI solution patterns; leads and develops talent; manages external delivery partnerships; and communicates clearly to influence decisions across organizational boundaries. This is a hybrid role requiring at least 10 office days per month. Candidates should reside within approximately 35–50 miles of the Madison, WI or Boston, MA office. New employees participate in orientation during their first week, in person at the Madison headquarters or another core location, with virtual sessions possible depending on scheduling and availability. Offers are contingent on applicable background checks and signing a nondisclosure agreement. Sponsorship will not be considered unless specified in the posting.

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 independently prototyping, reading and reviewing code, assessing analytical work, and leading 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 constraints, including prioritizing, sequencing, resizing, deferring, or declining work.
Experience running exploration 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 post-engagement ownership.
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 ability to lead through ambiguity, emerging technology, growth, and organizational change.
Ability to work from an office at least 10 days per month and reside within approximately 35–50 miles of Madison, WI or Boston, MA.

Responsibilities

Manage development and delivery of applied AI, machine learning, and advanced analytics solutions addressing high-value business problems.
Identify emerging technologies and capabilities that could improve solutions and provide a competitive edge.
Own team delivery commitments, sequencing, dependencies, risks, and quality, keeping work aligned with enterprise priorities.
Ensure solutions are production-oriented, maintainable, measurable, supportable, and designed for responsible use.
Shape, prioritize, and sequence a cross-domain portfolio with the Applied AI Director, peer leaders, and business stakeholders, considering capacity and dependencies and identifying opportunities for shared capabilities.
Translate ambiguous needs into clear problem statements, delivery plans, decision points, and measurable outcomes.
Contribute hands-on to proofs of concept, exploratory analysis, solution framing, merge request reviews, and other work where technical leadership accelerates learning.
Guide architecture and design decisions, review code and analytical work, and assess technical tradeoffs.
Set evidence-based criteria for moving work from exploration through proof of concept, pilot, production, scale, or closure.
Define success measures with business and product owners and connect technical performance to business outcomes.
Establish ownership, adoption plans, support models, escalation paths, monitoring expectations, and continuous improvement mechanisms for delivered capabilities.
Embed responsible AI practices 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.
Identify and redesign high-value workflows with business and technology leaders, connecting AI capabilities into coherent process journeys and clarifying changes needed to processes, data, technology, and adoption.
Identify repeated solution patterns and work with AI Platform Engineering to develop reusable frameworks, components, and delivery accelerators.
Reduce one-off builds where shared approaches improve speed, quality, governance, observability, and long-term supportability.
Build and lead a high-performing team of data scientists and applied AI practitioners aligned with enterprise strategic goals.
Set priorities and expectations, coach individual contributors, develop technical and leadership capability, and communicate strategic vision.
Create a collaborative, accountable, performance-driven, and inclusive work environment.
Manage vendor, consulting, contract staffing, academic, and research engagements, setting scope, outcomes, quality expectations, knowledge-transfer requirements, hand-off criteria, and post-engagement ownership.
Hold external partners accountable for responsible delivery while ensuring internal teams retain sufficient understanding and control of critical solutions.
Communicate technical concepts, uncertainty, risks, recommendations, tradeoffs, and business impact to executive, business, technical, and governance audiences.
Influence decisions across organizational boundaries, invite challenge, and adapt recommendations when supported by evidence.
Participate in New Employee Orientation during the first week of employment.