Machine Learning Scientist at Redesign Health

As our Machine Learning Scientist, you will be responsible for helping design, implement, prototype, train and evaluate custom ML models used by Redesign Health to help redesign the healthcare industry more optimally. This role is unique in terms of career growth as it combines large-scale ML modeling with high potential impact in an enormous yet inefficient industry. You will be performing cutting-edge research pushing the limits of large language models in order to achieve real-world healthcare impact.
In this role, you will focus more on new model creation and research versus deployment at scale or dataset creation. Some dataset tailoring to tasks at hand is expected. Most ML models will be trained on web-scale data, so knowledge of large-scale ML is a must.
Our machine learning models cover a myriad of NLP tasks that power the insights and investment strategies core to Redesign Health. These include, but are not limited to, text summarization, topic modeling, entity extraction and text classification. They also cover time series analysis and prediction and prediction on graph-structured datasets. Experience in either is very much desired though not essential.
You will report to and work with the VP of Machine Learning. The position can be fully remote within the United States or based out of our New York City headquarters.
What you'll do:


Experiment with large-scale NLP models, as required by product-features

Perform research deep dives into approaches used to solve particular NLP tasks that are currently needed. These include both deep learning and more classical approaches
Communicate the results of the deep dive with the broader team
Design and prototype the most promising models to see if they can be a game changer for the product feature du jour. Help navigate prototyping strategy with the broader team – e.g. in some cases we can leverage pre-training



Experiment with large-scale time-series analysis, as required by product-features

Perform research deep dives into approaches used to solve particular time series prediction tasks that are currently needed. These include both deep learning and more classical approaches
Communicate the results of the deep dive with the broader team
Design and prototype the most promising models to see if they can be a game changer for the product feature du jour. Help navigate prototyping strategy with the broader team



Help design products which are optimally adaptive to user needs out-of-the-box 

A big part of ensuring product fit is to calibrate the deployed models to the specific needs of the user. Help spearhead and research how we can optimally calibrate existing models so that they evolve to become more and more useful with more usage
Be an integral part of the discussion of how we measure success for deployed ML models
Help communicate and manage expectations for models currently used by our users



What you'll need:


Background

2-4 years of Machine Learning experience
2-4 years of Python experience
Knowledge of one popular deep learning library such as PyTorch, Tensorflow, Sklearn, Pandas, Keras, or Huggingface
Must have demonstrable outputs which are publicly available: e.g. an accepted ML paper, or a new product/feature that uses ML that you helped build
Experience in large-scale deep learning; model design, optimization (e.g. learning rate scheduling), hyperparameter tuning etc.
B.S. in Computer Science; PhD in Machine Learning is a plus





Technical Skills

Knowledge of basic probability, linear algebra, calculus and optimization
Ability to code advanced data structures in Python
Ability to build a large-language model from scratch in Pytorch or Tensorflow
Some experience with more classical NLP techniques such as LDA, PoS-tagging, anaphora resolution





Behavioral Skills


Collaborates. Works cooperatively with others across the organization to achieve shared objectives. Represents own interests while being fair to others and their areas. Partners with others to get work done. Credits others for their contributions and accomplishments. Gains trust and support of others. Building partnerships and collaborating with others to meet shared objectives.

Communicates Effectively. Is effective in a variety of communication settings: one-on-one, small and large groups, or among diverse styles and position levels. Attentively listens to others. Adjusts to fit the audience and the message. Provides timely and helpful information to others across the organization. Encourages the open expression of diverse ideas and opinions. Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences.

Action Oriented. Readily takes action on challenges, without unnecessary planning. Identifies and seizes new opportunities. Displays a can-do attitude in good and bad times. Steps up to handle tough issues. Taking on new opportunities and tough challenges with a sense of urgency, high energy and enthusiasm.