Innovation Manager, R&D

What You’ll Be Doing

• 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.

What We Look For

• 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.