Data Science Intern for a Fintech Start-up

Closed
Ubineer
Toronto, Ontario, Canada
CEO
(37)
4
Project
Academic experience
120 hours per student
Student
Canada
Intermediate level

Project scope

Categories
Data modelling Operations Financial modeling Investment Artificial intelligence
Skills
mathematics data pipelines parsing data science financial technology (fintech) natural language processing (nlp) hypertext markup language (html) extract transform load (etl) computer science machine learning
Details

Number of positions available: 1

Our team is seeking one bright Data Science/ML/AI students who is passionate and creative about Natural Language Processing. We want this student to algorithmically capture information from unstructured text or table data. The student will be working closely with the CEO of the company. This is an incredible opportunity for any student that is seriously/looking to build their resume and portfolio.

A bit more detail on the project:

Background:

If you are in your second last or last year of your stats/math/engineering/ computer science or equivalent program. The student must show passion and interested in ML / Data science and is seeking a rare opportunity to learn from industry experts.

If you like the sound of our culture and are ready to tackle this incredible challenge with us, then we'd love to hear from you.

Deliverables
  1. Algorithmically parse the correct text from an HTML file
  2. Algorithmically parse correct values from HTML tables
  3. Build functions as a service using 1 and 2 logic.
  4. If time permits create an efficient ETL data pipeline
Mentorship

We are seeking to offer 10-15hrs of mentorship

About the company

Company
Toronto, Ontario, Canada
2 - 10 employees
Technology, Banking & finance

Ubineer is a leading AI financial technology company focused on delivering productivity to financial decision makers. Our goal is to be the primary source for knowledge management, collaboration and insights in the investment industry. Our mission is to help financial decision makers optimize the quality and speed of their investment lifecycle.