Remote Aws Machine Learning Jobs in Springfield, MA

AVP Data Science - GD05AE

We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.

The Assistant Vice President (AVP), Applied AI leads data science, traditional machine learning, and agentic AI capabilities supporting The Hartford's Business Insurance. This role partners closely with underwriting, product, actuarial, and technology leaders to deliver scalable, production ready models and AI driven decision systems that support complex risks, bespoke products, and profitable growth across specialty markets.This role can have a Hybrid or Remote work schedule. Candidates who live near one of our office locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday) Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise. Must be eligible to work in the US without company sponsorship.Primary Job Responsibilities

• Own delivery, performance, and risk outcomes for one or more large, complex Applied AI portfolios spanning multiple teams, domains, or lines of business. Translate enterprise and businessunit AI priorities into multiyear portfolio roadmaps and investment plans.
• Ensure applied AI solutions deliver measurable business value while meeting standards for security, reliability, explainability, fairness, safety, and cost efficiency across solution types including generative and agentic AI, retrievalaugmented systems, forecasting, recommendation systems, anomaly or fraud detection, and multimodal use cases.
• Lead and develop Sr. Directors and Directors. Build leadership bench strength through succession planning, coaching, and capability development. Ensure consistent application of the Applied AI operating model, decision rights, delivery discipline, and escalation paths across the portfolio. Reinforce shared expectations for quality, evaluation rigor, and production readiness.
• Provide portfoliolevel technical direction and rigorous oversight, partnering closely with Principal ICs, Architecture, AI Platform, and Centers of Excellence. Ensure consistent adoption of approved AI standards, patterns, and guardrails.
• Review and thoughtfully evaluate portfoliolevel architectural choices, evaluation approaches, production readiness, and operational risk signals, guiding leaders through disciplined tradeoffs across quality, grounding, latency, cost, scalability, and regulatory risk.
• Accountable for consistent application of evaluation and monitoring practices across the portfolio. Ensure evaluation frameworks span classification, information retrieval, RAG/chat, forecasting, and customer or operational KPIs. Oversee governance of metric taxonomies, thresholds, validation evidence, gold and synthetic test sets, A/B testing practices, drift detection, failuremode analysis, and incident response expectations. Ensure evaluation results inform prioritization, release decisions, and risk management at the executive level.
• Set portfoliolevel expectations and governance for unstructured data and retrieval practices, including document ingestion pipelines, parsing, OCR, layoutaware extraction, metadata and lineage management, access controls, PII detection and redaction, and auditability. Ensure retrieval strategy decisions, including embedding approaches, hybrid and dense retrieval patterns, reranking, grounding validation, and multilingual considerations, align with enterprise standards and regulatory requirements.
• Accountable for portfolio-level AI governance ensuring alignment with Legal, Compliance, Model Risk, Privacy, Security, and Audit partners. Maintain readiness for audits and regulatory review by ensuring governance artifacts, controls, escalation paths, and operational evidence are consistently established and enforced. Escalate material risks, tradeoffs, and investment decisions to VPs with clear options and implications.
• Partner with senior leaders across Product, Technology, Operations, Claims, Underwriting, Finance, and HR to align Applied AI delivery with business outcomes. Influence portfolio funding, prioritization, and workforce planning through evidencebased assessments of delivery performance, evaluation outcomes, and risk considerations.
• Oversee portfoliolevel planning, dependencies, resourcing, and financial stewardship. Adjust plans to address shifting priorities, capacity constraints, emerging technical risks, or regulatory changes. Drive continuous improvement in delivery effectiveness, operational resilience, governance maturity, and value realization across the Applied AI portfolio.

Skills

• Demonstrated experience leading large, complex Applied AI portfolios in regulated enterprise environments.
• Proven ability to lead Sr. Directors and Directors, building durable leadership capacity and consistent operating discipline across organizations.
• Strong technical and regulatory fluency across applied AI, including generative and agentic AI, retrievalaugmented systems, evaluation and monitoring practices, and production AI operations, sufficient to review, inform, and govern seniorlevel decisions.
• Applied understanding of unstructured data and retrieval approaches, including document ingestion pipelines, OCR, layoutaware extraction, embeddings, hybrid and dense retrieval, reranking, metadata and lineage management, and PII controls.
• Deep familiarity with AI governance, model risk management, responsible AI practices, and compliancebydesign expectations.
• Demonstrated success translating strategy into coordinated execution and investment decisions across multiple teams over multiyear horizons.
• Ability to influence VPs and senior partners through clear, datadriven communication of technical tradeoffs, evaluation outcomes, portfolio risks, and business impact.

Education, Experience, Certifications and Licenses

• 12+ years of applicable experience with a Bachelor's degree; fewer years may be accepted with a higher degree. Master's or Ph.D. preferred in Machine Learning, Applied Mathematics, Data Science, Computer Science, or a similar analytical field, or progress towards a relevant professional designation.
• 7-10+ years leading leaders, large portfolios, or complex programs.

Compensation

The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford's total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:

$182,400 - $273,600

Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age

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