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Neil Shepherd
Growth leader in AI and technology companies
Neil Shepherd is a seasoned global growth leader known for his expertise in accelerating business growth and revenue in fiercely competitive sectors.
With a solid background in leading digital marketing, data analysis, and engineering teams, Neil has cultivated a reputation for success across various industries including Fintech, consumer services, and gaming.
His proficiencies span an array of specialties such as product management, commercialization of technology products, and launching innovative solutions like blockchain and IoT platforms.
Neil excels in driving large-scale digital marketing initiatives and demand generation strategies, achieving exceptional LTV/CAC ratios across B2C, B2D, SMB, and some B2B realms.
He is adept at spearheading data science and data engineering teams to extract rapid insights, managing substantial global marketing budgets effectively, and measuring ROI inclusively.
Neil Shepherd's skill set includes growth hacking, user journey optimization, segmentation strategies, and interventions along multi-touch funnels, showcasing his ability to navigate complex marketing landscapes.
He is known for his visionary approach, being proficient in building and coding diverse technologies such as mobile apps, embedded automotive systems, and IoT applications and platforms.
Neil's educational background features a MBA from Harvard Business School and a Bachelor's degree in Electronic Engineering from the University of Cambridge.
His professional journey includes pivotal roles at prestigious organizations such as Cohere, Scale AI, The Boston Consulting Group, PayPal, KIXEYE, QuinStreet, McKinsey & Company, and Simplexis.
Highlights
I recently co-wrote this article summarizing learnings from recent research into the impact of #generativeai and #LLMs on productivity. TLDR:
- Productivity gains can be very large. Approximately 14-50% improvement (and there are occasional outliers far beyond this) depending on the work type.
- Lower skilled or novice workers gain the most.
- Employee satisfaction and retention are improved.
- Large, additional gains will come from deep integration with enterprise data through RAG (Retrieval Augmented Generation - essentially referring to enterprise data when required, rather than a generic model's knowledge).
This space is moving incredibly quickly, and I'd love to hear from anyone working with LLMs that's seeing impact - this will be the first in a series of many articles: https://lnkd.in/gTc_UdPd
Hey Linkedin folks --
I'm working with a really high growth #ai company doing some really exciting things to provide AI as a service for text classification, summarization, generation and other use cases.
They're looking for #documentation #writers and a lead - contract or FT. Any leads? Great opportunity for someone looking to get into AI, as deep AI knowledge isn't needed.
