Define and evolve Wiser's AI and data science technical strategy in partnership with product and business leadership
Represent Wiser's AI capabilities to customers, partners, and advisors—articulating our approach, roadmap, and differentiation
Identify high-impact opportunities where AI can solve customer problems or create competitive advantage
Establish technical standards, patterns, and best practices that influence engineering decisions across the organization
Architect and build production AI systems including LLM applications, RAG pipelines, semantic search, and traditional ML models
Design rigorous evaluation frameworks, experimentation methodologies, and monitoring systems that ensure AI solutions deliver reliable, measurable results
Bridge classical data science approaches (statistical modeling, experimentation design, feature engineering) with modern generative AI techniques
Own technical quality for AI systems end-to-end: from data pipelines through model deployment to production observability
Partner with product management to translate business requirements into technical approaches and validate solutions against customer needs
Mentor and elevate the AI/data science team (3-5 engineers), raising the technical bar through code review, architecture guidance, and knowledge transfer
Collaborate across engineering teams to integrate AI capabilities into Wiser's broader platform architecture
Drive build-vs-buy decisions and vendor evaluations for AI infrastructure and tooling
Champion AI-native development practices across Wiser engineering—demonstrating how AI tools accelerate development, improve code quality, and change what's possible
Help build an engineering culture where AI augmentation is the default, not the exception

Requirements

15+ years of experience in data science, machine learning, or ML engineering, with demonstrated progression into technical leadership
Deep expertise in statistical methods, experimental design, and classical ML (not just LLM integration)
Proven ability to architect and deliver production ML/AI systems at scale on cloud platforms (AWS strongly preferred)
Strong software engineering fundamentals: you write production-quality code, not just notebooks
Track record of organization-wide technical influence—setting standards, driving architectural decisions, mentoring engineers
Experience communicating technical strategy and capabilities to non-technical stakeholders, including customers and executives
Demonstrated ability to operate autonomously, identify high-impact problems, and drive initiatives without close direction
Active, daily use of AI coding assistants (Cursor, Claude Code, GitHub Copilot) and a demonstrated belief that AI fundamentally changes how software gets built.
Technical Depth Expected: NLP and text analytics: embeddings, semantic similarity, classification, entity extraction, and information retrieval
LLM integration patterns: prompt engineering, RAG architectures, agent frameworks, evaluation methods
Data engineering: pipeline design, data quality, feature stores, and working with large-scale datasets
MLOps: model deployment, monitoring, A/B testing infrastructure, and production observability
Python ecosystem mastery; SQL fluency; comfort with distributed systems concepts
Preferred: Experience with transformer architectures, fine-tuning, or training custom models
Background in retail, e-commerce, or product data domains
Familiarity with LLM orchestration frameworks (LangChain, LlamaIndex) and vector databases
Track record of introducing AI-augmented workflows to teams or organizations
Opinions (and evidence) on how AI changes engineering practices, team structures, or development processes

Benefits

Performance-based discretionary bonuses and variable pay plans available for some positions

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data sciencemachine learningML engineeringstatistical methodsexperimental designproduction ML/AI systemsNLPMLOpsPythonSQL

Soft Skills

technical leadershipcommunicationmentoringautonomyinfluencecollaborationproblem-solvingstrategic thinkingcode reviewknowledge transfer