Quantitative Data Scientist

Numerai

Numerai is a new kind of hedge fund powered by a decentralized network of machine learning models. Every week, thousands of data scientists from around the world compete in the (Use the "Apply for this Job" box below). data science tournament to model our dataset and predict the global stock market, earning staking rewards in the NMR cryptocurrency based on their performance. Collectively, these staked predictions form the Meta Model which controls the portfolio of the Numerai hedge fund.

We are looking for talented and motivated data scientists, data engineers, and machine learning researchers to help us capture all of the data in the world and convert it into alpha.

The Stack

• Python (Pandas, Numpy, Scikit-Learn)

• AWS (S3, EC2, Batch)

• Airflow

• Terraform, Docker

• Postgres, MySQL

The Role

Your role as a quantitative data scientist is to gather all of the data in the world and convert it into alpha.

You will be asked to source new interesting datasets, build models to determine their value, and research how to best incorporate them into our strategy and our data science tournament dataset.

As a senior engineer, you will be expected to own and lead major projects while actively raising the engineering bar across the entire organization. You will work with other senior engineers to architect robust and performant systems and processes.

Example Projects

• Research, trial, purchase, and integrate new datasets to further enrich our equities data collection, and enable better prediction of stock movement.

• Systematize transformations to automate the process of onboarding new data to enable higher throughput and more reliable data science.

• Research, implement, and test transformations to the dataset to improve machine learning performance.

Requirements

• Experience training and deploying machine learning models

• Experience with quant finance data

• Experience building data platforms

• Excellent written communication (design docs, specs, documentation, code reviews, post-mortems)

• Extreme ownership

• Good general systems knowledge and debugging skills

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