Applied AI Senior Manager

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 realiz