Sr. Director, Data Science & AI

Vuori, Inc•Carlsbad, CA

1d•$215,300 - $288,225•Onsite

About The Position

As we accelerate our transformation into an enterprise powered by data and AI, we are seeking a Senior Director of Data Science & AI to serve as the strategic architect and operational leader of our data science and artificial intelligence-based solutions. This role goes well beyond managing models—it is about shaping how AI fundamentally transforms how we understand our consumers, optimize our operations, and make decisions across every channel and market. Reporting directly to the VP of Analytics & Planning, you will own the enterprise AI and data science strategy, build and scale a world-class team, and drive adoption of AI-powered solutions across merchandising, supply chain, marketing, consumer experience, and international expansion. You will be a key member of the analytics leadership team, influencing executive decision-making and championing a culture of experimentation and data-driven innovation across the organization.

Requirements

15+ years of progressive experience in data science, machine learning, or advanced analytics, with at least 5+ years in senior leadership roles managing multi-disciplinary teams.
Proven track record of building and scaling an enterprise AI/data science function from the ground up or through significant transformation.
Deep expertise applying AI/ML to consumer behavior, personalization, lifecycle marketing, and demand intelligence in a B2C, retail, or DTC environment.
Demonstrated success delivering production-grade ML systems at scale—not just prototypes, but models running in production driving measurable business outcomes.
Strong executive communication skills with the ability to influence C-suite stakeholders, translate complex technical concepts into business narratives, and secure investment for AI initiatives.
Hands-on technical depth in Python, SQL, and modern ML frameworks (scikit-learn, PyTorch/TensorFlow, Spark), with the judgment to balance technical excellence with pragmatic delivery.
Experience with cloud-native ML infrastructure (Snowflake, Databricks, AWS SageMaker, or equivalent) and modern MLOps tooling.
Master’s or PhD in a quantitative discipline (Statistics, Machine Learning, Computer Science, Applied Mathematics, or related field).

Nice To Haves

Retail apparel, athleisure, or lifestyle brand experience where brand affinity, style preferences, return behavior, and seasonal dynamics are critical.
Experience with modern MarTech and CDP ecosystems (Hightouch, Braze, Attentive, Salesforce, or similar) and embedding AI into marketing activation workflows.
Exposure to generative AI applications (LLMs, RAG architectures, AI assistants) in enterprise or consumer contexts.
Familiarity with real-time ML serving and personalization engines for web, app, and in-store experiences.

Responsibilities

Enterprise AI & Data Science Strategy
Define and own the multi-year AI and data science strategy aligned to enterprise growth priorities, presenting roadmaps and business cases directly to the C-suite and board.
Establish the organization’s AI vision—identifying where machine learning, generative AI, and advanced analytics create the highest-leverage impact across the value chain.
Lead the evaluation and adoption of emerging AI capabilities (LLMs, agentic AI, computer vision, NLP) and determine fit-for-purpose applications across business domains.
Drive the build-vs-buy strategy for AI/ML platforms and tools, partnering with engineering leadership to shape the technology ecosystem.
Operational Leadership & Execution
Translate strategic priorities into an actionable portfolio of AI/data science initiatives with clear business KPIs, timelines, and resource plans.
Own the end-to-end lifecycle of AI/ML solutions: ideation, experimentation, development, productionization, monitoring, and continuous improvement.
Establish scalable MLOps practices including model versioning, automated retraining pipelines, drift detection, and governance frameworks.
Create and maintain an AI use case registry that tracks initiative status, business value delivered, and prioritization across all domains.
Consumer Intelligence & Personalization
Architect the enterprise consumer intelligence strategy—unifying customer data signals to build a holistic understanding of consumer behavior, preferences, and lifetime value.
Drive production-grade AI solutions including:
Advanced customer segmentation (behavioral, psychographic, ML-based clustering)
Customer lifetime value modeling and predictive retention frameworks
Hyper-personalized recommendations across web, app, email, and in-store
Churn prediction & proactive win-back strategies powered by real-time signals
Next-best-action engines for lifecycle marketing orchestration
Multi-touch attribution & media mix modeling to optimize marketing spend
Partner with Marketing, CRM, and Digital Product to embed AI into consumer-facing experiences and campaign optimization.
Cross-Functional Influence & Business Partnership
Serve as the senior analytics thought partner to business leaders across Merchandising, Supply Chain, Retail, Wholesale, International, and DTC channels.
Identify and champion AI-driven opportunities in demand forecasting, inventory optimization, assortment planning, pricing intelligence, and supply chain efficiency.
Collaborate with Finance and FP&A to integrate predictive models into planning cycles, scenario analysis, and strategic decision frameworks.
Influence and align with Data Engineering, Data Platform, and Enterprise Architecture teams to ensure infrastructure supports scalable, production-ready AI workloads.
Represent the AI and data science function in cross-functional steering committees, vendor evaluations, and strategic planning sessions.
Team Building & Organizational Development
Build, lead, and mentor a high-performing team of data scientists, ML engineers, and AI specialists across consumer, operational, and emerging AI domains.
Establish a center of excellence for AI and advanced analytics with reusable frameworks, shared playbooks, and governed best practices.
Create clear career development paths and a talent strategy that attracts and retains top-tier data science talent in a competitive market.
Foster a culture of intellectual curiosity, rigorous experimentation, and business-outcome orientation across the team.
Drive knowledge sharing and AI literacy initiatives to elevate data science capabilities across the broader analytics organization.
Measurement, Governance & Ethical AI
Define and own the impact measurement framework for all AI initiatives—tracking ROI, business value, and operational lift with rigor and transparency.
Implement robust A/B and multivariate testing programs to validate personalization, targeting, and optimization strategies before scaling.
Champion responsible AI practices including fairness, explainability, bias detection, and compliance with data privacy regulations (CCPA, GDPR).
Establish AI governance standards including model documentation, approval workflows, and regular performance audits.