Abzu is breaking new ground in artificial intelligence with notable successes in the life sciences and pharmaceutical industries. We are looking for an engaging communicator experienced with ML and AI to help establish Abzu as a thought-leader in transparency, explainability, and ethics.
Big plusses if you have life science experience, an established digital presence, and style.
Abzu Evangelist | Science Communicator
You’ll be an integral part of Abzu’s voice in a market inundated with AI overstatements and Big Tech’s black box smoke and mirrors. You’ll be a bridge between our data scientists, bioinformaticians, and computational chemists and our marketing and communications team.
In-person and digitally, you’re an outgoing and engaging communicator with no need to roll a charisma check. As a conduit for community, you’re comfortable making connections with anyone interested in applying AI from baby data scientists to data science specialists. You have enough real-world experience to answer technical questions and the nerve to admit when you can’t.
You can lead technical training sessions and share the stage with subject matter experts. You’re at ease making connections with professors, researchers, and students as you’re all about spreading Abzu’s love of scientific exploration. You’ve never made anyone fall asleep because you’re an amazing facilitator and science is cool!
You know which AI and biotech conferences and events to participate in. You can speak on both industry-specific technical concepts (e.g., technical applications of AI) and general concepts (e.g., the dangers of black box AI, AI ethics, AI regulation, etc.). You’re not a coordinator, speaking prospectus monkey, or booth jockey; instead, you have a handful of catchy session topics and the ability to command a keynote.
You’ll be the face of Abzu in digital communities, not only sharing case studies and success stories, but creating Jupyter notebooks and generating interest in using Abzu’s community QLattices.
- You bring +5 years of experience working as a data scientist. You have experience with Python (sklearn, statsmodels, pandas, numpy) and statistical analysis.
- You love speaking, teaching, and training. You’re a scientific social butterfly with a passion for sharing the latest on AI, machine learning, and research.
- You’re friendly, communicative, and funny, and can be tastefully provocative.
- You believe in Abzu’s position on AI and ethics, and you can confidently and competently speak against black box AI and black box modelling.
We’d love it if
- You have bioinformatics, clinical trial, health economics, or similar real-world evidence data experience.
- You’re a curious and ambitious good human.
- You can tell us a story about increasing someone’s sense of wonder.
Abzu was born from the desire to challenge the fundamental assumptions of contemporary, black box AI. Abzu’s pioneering artificial intelligence, the QLattice, accelerates analysis and insights through transparent and explainable models and inspires data scientists to be more scientific.
Founded in January 2018, Abzu is a deep tech startup with offices in Copenhagen, Denmark and Barcelona, Spain.
You’ll have the opportunity to be part of a highly-skilled, fast-thinking, and energetic team. Our philosophy and culture are based on self management, which empowers people to be themselves and make their own decisions about their work.
We are focused on creating the best environment where people have integrity, a strong sense of responsibility, and collaborate in creative and inspiring ways. We believe and trust in people, their high potential, and inestimable value.
This requires transparency and trust, which is an integral part of our technology and who we are. This organizational style is not a fit for everyone.
Our mission is to build a machine where cognition arises through self-organization of millions of interactions. We are bringing explainable artificial intelligence to researchers and scientists worldwide.
With the rapid advancement of machine learning, anyone can fit a model to a dataset. However, it is still too complicated to explain what is going into a decision, why a specific outcome happens, and what's truly important.
Our graph-based technology is currently used in the life science and pharmaceutical industries to find answers to problems by leveraging our technology’s "explorative" ability to test millions of potential models and seamlessly pick the simplest and most accurate ones.
As a result, researchers and scientists can test hypotheses faster than ever using one standardized machine learning framework to interpret and explain what is going into a decision (and not just predicting it).
We look forward to hearing from you!