Position: Data Scientist / Machine Learning Engineer
Company: Monosoft
Experience required: 3y
English level: fluent

**About the project**

The project's advanced AI technologies in the field of illiquid and private debt markets makes it easier for traders, executives, and decision-makers to search and see personalized information, perform quick calculations on supporting data, and ultimately support the complex financial decisions that surround the complex alternative and private debt assets they are looking to buy and sell.

This is possible through the use of number of propriety technologies, including  robotic advisor, chatbots, ML algorithms, and AI models that can utilize historical and current behaviors along with financial quantitative models that are then combined with data from our knowledge base, or from an API of one of our data partners in order to provide insightful guidance and support for day-to-day decisions.

**About the position**

The Data Scientist + AI/ML engineer role is responsible for structuring the data, Machine Learning, Artificial Intelligence, Natural Language Processing, and data normalization and delivery features of the proprietary Fintech platform.

As part of a lean team, you will be given the opportunity to take ownership of your work, work autonomously and help influence the direction and build-out of a creative FinTech platform. You should be able to work both independently and collaboratively with a scrappy startup mentality.

**Requirements**

- Bachelor's degree in computer science, data science, mathematics, or a related field.
- In-depth knowledge of statistics, multivariable calculus, linear algebra and algorithms
- Advanced proficiency with Python and R code writing.
- Working knowledge of Microsoft Azure.
- Extensive knowledge of ML frameworks, libraries, data structures, data modelling, and software architecture.
- Experience with data querying languages, and statistical or mathematical software.
- Advanced coursework in machine learning and programming is a plus.

**Responsibilities**

- Consulting with managers to determine and refine machine learning objectives.
- Structure, organize, and deploy interconnected data-based infrastructure
- Communicate data and ML needs to the team in a comprehensive and clear manner
- Creating machine learning models.
- Developing ML algorithms to analyze huge volumes of historical data to make predictions.
- Documenting machine learning processes.