Boston Scientific - Marlborough, MA
posted 3 months ago
At Boston Scientific, we are seeking a passionate, innovative, and results-oriented Learning Design Specialist to join our team. This role is pivotal in shaping the learning experiences of our urology sales organization. You will collaborate with Sales Training Managers (STMs), product marketing, and other cross-functional partners to develop engaging and effective learning solutions tailored to the needs of field-based learners and the business. As part of the Learning and Design team, you will also work with STMs to develop and implement training workshops for National Sales Meetings and other necessary meetings as determined by the commercial teams. This position requires an individual who thrives in a fast-paced, growing organization and demonstrates agility and adaptability. Your responsibilities will include designing and developing high-quality learning materials with clear performance-based objectives, conducting needs assessments in partnership with Marketing, Sales, and Operations teams, and creating comprehensive learning curricula that align with organizational goals. You will design engaging and interactive learning experiences that leverage various instructional strategies and technologies, ensuring that learning experiences are sequenced logically and scaffolded appropriately. Additionally, you will use Articulate360 to develop digital content for our LMS/LXP and collaborate with the Learning Technologist to deliver dynamic and interactive learning experiences. Quality assurance is also a key aspect of this role, as you will conduct thorough reviews to ensure the accuracy and effectiveness of learning materials, implementing feedback from stakeholders to continuously improve the learning experience. You will manage multiple projects simultaneously, ensuring that deliverables are completed on time and within budget, and develop evaluation strategies to measure the effectiveness of learning interventions. Your ability to analyze data and feedback will be crucial in identifying areas for improvement and making recommendations for future iterations.