The rise of AI doesn’t have to spell doom for data careers.
In our latest Data Chats podcast episode, Favio Vazquez, senior data scientist at H2O.ai, not only provides reassurance to data professionals but also presents new career opportunities.
Favio highlighted three emerging roles with the potential to lead the merging of human and machine intelligence in business.
With his inside perspective, Vazquez shines a light on these novel positions that uniquely combine technical expertise and business acumen, and we’ll outline them in this article.
During the podcast, he also discussed auto-machine learning, ethical approaches to AI and ML, and agile data science.
Listen to the full episode:
Prompt Engineering
Favio explains, “Prompt engineers are people who really know how to talk to these large language models. It might seem that it’s super easy, but if you really want to find value, you need to understand how to build these statements, and there’s a whole science behind it. And there are people who really understand how to create these prompts to get the best results.”
The prompts input into AI models significantly impact the output. Prompt Engineers are crucial in bridging human intentions with AI capabilities through optimized prompts. Their expertise makes generative AI more useful and aligned with business goals.
As businesses continue adopting AI, demand grows for skills in implementing it effectively. Prompt Engineers who can connect teams with AI systems provide immense value. Their role will only gain importance as AI investment accelerates.
Essential Skills for Prompt Engineers
- Understanding of Machine Learning and AI: A solid grasp of machine learning principles, algorithms, and the underlying mechanisms of AI.
- Effective Communication: Ability to articulate thoughts clearly and craft prompts that accurately convey the intended query or command to the AI model.
- Understanding of Semantics and Syntax: A good grasp of language semantics and syntax helps create effective prompts that elicit accurate model responses.
- Critical Thinking: The ability to analyze situations, identify problems, and devise solutions is crucial for crafting effective prompts.
- Data Analysis: Skills in analyzing and interpreting complex data can aid in understanding the responses from AI models and refining prompts for better accuracy.
- Keeping Abreast of Technological Advancements: The field of AI is rapidly evolving; thus, a willingness and ability to learn and adapt to new technologies and methodologies is essential.
- Communication with Non-Technical Stakeholders: Ability to translate complex technical concepts into understandable terms for non-technical stakeholders, facilitating better decision-making.
- Understanding of Ethical Implications: Awareness of the ethical implications associated with AI and the potential biases in model responses is essential for responsible prompt engineering.
Decision Scientists
Decision Scientists infuse data-driven insights into the development process, ensuring the products align with market demands and organizational objectives.
Decision Scientists use the prudent application of Machine Learning (ML) to improve decision-making. They harness the predictive and analytical power of ML to furnish actionable insights, aiding in more informed and strategic decisions.
Here is how Favio describes decision scientists: “They know machine learning, so now they can add value in a different way. They add value by helping companies put this model into production, explain things, communicate results, help make decisions, and inform.”
Essential Skills for Decision Scientists
- Strong analytical abilities: Proficiency in statistical modeling and machine learning to extract insights from data.
- Business acumen: Understanding company operations, objectives, and challenges to transform analytics into strategic recommendations.
- Communication skills: The ability to clearly and persuasively convey data insights to diverse audiences.
- Strategic mindset: Identifying high ROI use cases and optimal solutions to drive business value.
- Collaboration: Working cross-functionally to understand needs and ensure the adoption of solutions.
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Business Scientists
Leveraging their business experience, business scientists translate organizational challenges into data-centric queries.
Favio explains the new role: “So they will have all this experience and knowledge gained from years of being in the company and dealing with its problems and pains. And now they also understand AI. So they can take all of that real-world information and business knowledge and apply data science and AI to help solve problems.”
Business Scientists are at the nexus of business acumen and data expertise, applying data science methodologies to unravel and address complex business challenges.
Their role transcends mere data analysis to encompass a deeper understanding of business operations, formulating data-driven strategies that propel business growth and efficiency.
Essential Skills for Business Scientists
- Statistics and machine learning fundamentals to analyze data and build models
- Programming skills for manipulating, analyzing, and visualizing data
- Business and industry knowledge to contextualize analytical insights
- Leadership abilities to guide strategic business decisions
- Communication skills to distill complex concepts for diverse audiences
- Ability to identify and clearly define key business challenges and opportunities
Data Science for Business Leaders Can Help You Leverage Your Business Acumen in New Ways
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