Director, Business Transformation

Celsius•Boca Raton, FL

4d•Hybrid

About The Position

As the Director, Business Transformation, you'll be at the forefront of innovation and growth across the CELSIUS®, Alani Nu®, and Rockstar® portfolio. Reporting to the Chief Business Transformation Officer, this is a hands-on leadership role: you will design, build, and deliver AI and advanced-analytics capabilities that change how the company makes decisions — then redesign the processes and drive the adoption that turn those capabilities into results. This role blends technical depth with business fluency. You are comfortable in the tooling — writing prompts, wiring agents to data and systems, and testing until the output is trustworthy enough to run the business on — and equally comfortable mapping an end-to-end business process, finding where the friction and the value actually sit, and redesigning the workflow around the new capability. Technology alone doesn't move the number; the process change around it does. You'll operate as both a builder and an orchestrator. Some solutions you'll prototype yourself; others you'll deliver through AI vendors, systems integrators, and internal IT and data partners. Your hands-on experience is what makes you an effective buyer — able to evaluate vendor claims critically, scope work accurately, hold partners to real performance standards, and know when to build, when to buy, and when to do nothing. Finally, you'll drive the change that makes any of it stick. Adoption is the deliverable, not deployment. You'll lead the change management around each release — stakeholder alignment, process documentation, training, and reinforcement — and multiply the capability across the organization by teaching functional teams in Sales, Marketing, Finance, Operations, and beyond to build within BTO guardrails, so the BTO becomes an accelerant rather than a bottleneck.

Requirements

5+ years in business transformation, analytics, data, or technology roles, including direct, hands-on delivery of AI, machine-learning, or advanced-analytics solutions that produced measurable business results. Portfolio or walkthrough of systems you personally built is strongly preferred.
Hands-on AI build experience (required): Demonstrated experience personally building and deploying AI agents and LLM-powered applications, not solely sponsoring or overseeing them. This includes prompt engineering and prompt architecture, system-prompt and instruction design, tool/function calling, retrieval-augmented generation (RAG), context and memory management, agent orchestration, and evaluation or testing frameworks to measure output quality and reduce hallucination risk.
Business process and operating model expertise: Proven ability to map, analyze, and redesign end-to-end business processes — identifying where value, cost, and friction actually sit, and re-engineering the workflow around a new capability rather than layering technology on top of a broken process. Experience with process documentation, requirements gathering, and enhancing existing enterprise systems and workflows.
Vendor and partner management: Track record of evaluating, selecting, and managing AI and technology vendors, systems integrators, and consulting partners — running structured evaluations and pilots, writing clear statements of work and success criteria, negotiating alongside procurement and legal, and holding partners accountable to performance, cost, and timeline. Sound build-versus-buy judgment informed by hands-on technical understanding.
Change management and adoption: Demonstrated success driving organizational adoption of new capabilities — stakeholder alignment, communication planning, training and documentation, and reinforcement — with the ability to influence senior leaders and frontline teams without direct authority.
Applied fluency with frontier AI platforms: Practical, day-to-day experience building on platforms such as Anthropic Claude (Projects, Skills, MCP, Claude Code, or the API), OpenAI/ChatGPT (Custom GPTs, Assistants, or the API), Google Gemini, or equivalent — including building working dashboards, analytical tools, and internal applications on top of them.
AI and data infrastructure: Working knowledge of the plumbing behind production AI — data pipelines and modeling, APIs and integrations, vector databases and embeddings, orchestration frameworks, version control, environment and cost management, and the security, access, and governance controls required to run AI safely on enterprise data.
Technical literacy: Proficiency in SQL and working proficiency in Python (or equivalent) sufficient to prototype independently, inspect a model or pipeline, and hold a credible technical conversation with engineering and data science partners.
Breadth across the AI landscape: A broad, current understanding of the AI ecosystem — model capabilities and trade-offs, build-vs-buy decisions, emerging tooling, and cost/performance dynamics — paired with the judgment to select the simplest solution that solves the business problem.
Enablement and teaching ability: Proven success upskilling non-technical business partners — running training, building reusable prompt and template libraries, and coaching functional teams to independently build and maintain their own AI-assisted workflows.
Bachelor's degree required; an MBA or a degree in a technical or quantitative field is a plus.
Demonstrated sustained performance over time in a senior- or director-level role within a complex enterprise environment.
Proven cross-functional partnership across sales, commercial, marketing, operations, finance, category & insights, HR, and investor/public relations.
Fluency with modern data and AI stacks, including cloud data platforms such as Snowflake or equivalent, BI/visualization tools, and applied AI/ML or enterprise AI platforms.

Responsibilities

Prototype and ship, hands-on. Personally build AI agents, analytical tools, and internal applications where speed and proximity to the business matter — writing the prompts, configuring the tools and integrations, connecting them to Snowflake and source systems, and iterating with users until adoption is real.
Lead end-to-end AI implementation across functions, from use-case identification and scoping through build, deployment, adoption, and ongoing performance monitoring.
Redesign the process, not just the tool. Map current-state workflows, quantify where time and cost are lost, and re-engineer the future-state process around the new capability — including the upstream data, downstream decisions, roles, and controls that determine whether it actually delivers.
Evaluate and manage AI vendors and delivery partners. Run structured evaluations and pilots, make defensible build-versus-buy recommendations, define scope and success criteria, and manage partners and systems integrators to cost, quality, and timeline in partnership with IT, procurement, and legal.
Own adoption and change management. Lead the stakeholder alignment, communications, training, documentation, and reinforcement that turn a deployed capability into a habit — and track adoption and business impact as primary success measures, not deployment alone.
Stand up and maintain the AI infrastructure and standards that make delivery repeatable: reusable agent and prompt patterns, a shared component and template library, evaluation and QA methods, documentation, access and security controls, and cost monitoring.
Enable the enterprise to build. Design and run the AI enablement program for Celsius Holdings — hands-on training, office hours, prompt and use-case libraries, and embedded coaching that equips functional teams to create their own solutions within BTO guardrails.
Partner with embedded Transformation Leads in Sales, Commercial, Marketing, Operations, Finance, Category & Insights, HR, and Investor & Public Relations to identify high-value opportunities and translate business problems into AI, machine-learning, and agent-based solutions.
Sequence for cost discipline. Drive a crawl-walk-run roadmap that banks cheap, high-impact wins first and reserves expensive computing for use cases with clear payback. Establish ROI gates and manage cost-per-insight as a tracked metric.
Keep decisions aligned. Run the cross-functional operating cadence, shared dashboards, a common metric library, and a decision log, so commercial calls are made in days, not weeks, and functions work from one version of the truth.
Build on the Snowflake data foundation to move each department from descriptive dashboards to predictive models and prescriptive or generative capabilities that recommend the next best action.
Deploy and orchestrate AI agents on enterprise platforms such as Salesforce Agentforce/Einstein, ServiceNow, SAP Joule, or Microsoft Copilot where appropriate, integrating them with existing workflows so they are used in the flow of work rather than alongside it.