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About the role
Key responsibilities & impact- Identify and evaluate new revenue‑generating opportunities by engaging with customers, industry partners, and internal stakeholders across Clario.
- Lead discovery and concept development, turning unmet needs and early ideas into clear problem statements, opportunity briefs, and high‑level solution concepts.
- Deeply explore the AI and data space (e.g., machine learning, generative AI, computer vision, workflow automation) to identify innovations and integrations that can drive new revenue streams and operational efficiencies.
- Partner closely with AI/ML and data science teams to: Understand current capabilities and roadmaps, Brainstorm new AI‑enabled features and services, Assess where and how AI can be safely and effectively embedded into existing and new products.
- Define and maintain an innovation opportunity portfolio / roadmap that prioritizes concepts (especially AI‑enabled ones) based on business value, feasibility, risk, and strategic alignment.
- Facilitate ideation workshops, design sprints, and discovery sessions with AI, product, engineering, and commercial teams to refine concepts and converge on the most promising solutions.
- Collaborate with architects and senior engineers to assess technical options, constraints, and high‑level solution approaches, without directly owning coding or detailed system architecture.
- For high‑potential opportunities, define experiment/MVP charters that capture: The customer problem and target users, Key hypotheses and assumptions, Desired outcomes and high‑level success criteria, Risks, dependencies, and key metrics to observe.
- Work closely with the Product Owner to: Translate opportunity briefs and MVP charters into product backlogs and user stories, Ensure the original problem, hypotheses, and success criteria are understood by delivery teams, Review learnings from increments and experiments and iterate on concepts as needed.
- Analyze results from experiments, prototypes, and customer feedback to recommend whether to scale, pivot, or stop initiatives, with a focus on measurable revenue impact and efficiency gains.
- Prepare and present concise business cases and recommendations to leadership and product stakeholders, summarizing opportunity size, risks, learnings, and proposed next steps.
- Champion a culture of experimentation, learning, and “productive failure”, ensuring that insights from both successful and unsuccessful initiatives are captured, shared, and used to inform future innovation work.
Requirements
What you’ll need- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field; an advanced degree or MBA is a plus.
- 7+ years of experience in software, data, or product environments (e.g., engineering, solution architecture, product strategy, innovation/R&D, consulting), including time spent in discovery / early‑stage concept work.
- Strong AI and data literacy: familiarity with concepts such as machine learning, generative AI, computer vision, and workflow automation, and an ability to discuss capabilities, limitations, and appropriate use cases with technical experts.
- Proven track record of identifying and shaping new product or capability opportunities, ideally including AI‑enabled or data‑driven solutions, and seeing them through early validation.
- Experience engaging directly with customers, partners, and internal stakeholders to uncover pain points, synthesize insights, and translate them into clear problem statements and opportunity briefs.
- Demonstrated ability to collaborate effectively with AI/ML, data science, product management, and engineering teams, influencing without direct authority.
- Comfort working in Agile environments, partnering closely with Product Owners and delivery teams, and using experiments/MVPs to test hypotheses.
- Strong analytical and strategic thinking skills, with the ability to size opportunities, evaluate trade‑offs, and connect innovation initiatives to measurable business outcomes (revenue, efficiency, differentiation).
- Excellent communication and storytelling skills, capable of framing complex ideas simply, leading workshops, and presenting concise recommendations to senior stakeholders.
- Mindset oriented toward experimentation, learning, and “productive failure”, with the resilience to iterate based on feedback and data.
- Experience in regulated healthcare, life sciences, or clinical trials is a strong plus; curiosity and willingness to learn this domain are essential.
Benefits
Comp & perks- N/A 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score
ATS Keywords
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Hard Skills & Tools
machine learninggenerative AIcomputer visionworkflow automationdata analysisproduct strategysolution architectureinnovationAgile methodologiesMVP development
Soft Skills
analytical thinkingstrategic thinkingcommunicationstorytellingcollaborationinfluencingproblem-solvingresiliencefacilitationcuriosity
Certifications
Bachelor’s degree in Computer ScienceBachelor’s degree in EngineeringBachelor’s degree in Data ScienceMBA