Machine Learning Scientist

Machine Learning Scientist
Location: New York, NY

What We Do
Arena AI is a startup committed to bringing autonomy where it is needed the most. Our products have helped some of the world’s largest CPG companies transform their sales and supply chains into increasingly autonomous, self-learning systems — unlocking efficiencies and resulting in better, more affordable consumer experiences.

But autonomous selling is just the beginning. Arena knows that Autonomy can help organizations tackle much more, from delivering critical goods and services more affordably, sustainably, and equitably to the world, to reducing green premiums on renewable energy to compete more effectively with fossil fuels.

*How You’ll Help *

As a Machine Learning Scientist at Arena, your responsibilities will include developing machine learning systems that leverage uncertainty estimation and active learning approaches like Reinforcement Learning to solve real-world problems faced by our customers. Your work will span the spectrum from quick exploratory/experimental models to scaled, production processes.

Your work may start from simple models, but the ultimate goal is to push the boundaries of what is scientifically and technically possible, and share those advances with the greater scientific community via publication and/or open source.

Basic qualifications:

• Deep passion for AI and building machine learning systems
• Interested in the full ML stack from initial R&D through deploying models into production
• Comfortable working with data in Python and SQL
• Passionate about good engineering, clean code, and scalability
• Able to communicate technical concepts clearly at all levels, whether that be engaging deeply on a topic with other Machine Learning Scientists or sharing key insights with business stakeholders

Nice to haves:

• Experience with active learning, reinforcement learning, causal inference and/or online learning approaches
• Experience with serverless or highly distributed Machine Learning model training on massive datasets
• Expertise in experimental design and Bayesian uncertainty estimation
• Familiarity in deep learning frameworks (e.g. PyTorch/TensorFlow)