About You
As a Data Scientist at Tide, you will split your time between statistical analysis, identifying and leveraging new data sources, identifying new applications for data science as well as learning about various data technologies. You will be working closely with the business as well as the engineering teams in order to deliver business value within an agile framework.
You will join our data science community of practices and your input on how to improve processes and maintain a high quality will be very welcomed. Career progression is as important to us as it is for you! With our expanding teams and business we will encourage you to outgrow your initial responsibilities, if you so desire. This role offers an exceptional opportunity to make a real difference with responsibilities across engineering practices in a rapidly expanding company!
Some of the things you’ll be doing:
- Working closely with our Business Services team in order to build payment classification and data extraction use cases
- Understanding business requirements and solutionize data products in order to solve them
- Identifying creative solutions in order to build relevant training data sets
- Training models and optimize hyperparameters
- Working closely with our data engineers in order to productionize models
You’ll be a great fit if:
- You've expertise preferably with Python or other languages such as R, Julia.
- You 're incredibly analytical with strong mathematical skills.
- You've strong knowledge of machine learning methods.
- You've some experience in data science/machine learning libraries(e.g. XGBoost, etc.)
- You've strong desire to learn and apply new techniques or solutions.
- You're able to interpret and analyze data problems.
- You've experience with version control and code repositories (e.g., Bitbucket, Git, GitHub, Markdown).
- You're able to collaborate with data and software engineers to enable deployment of sciences and technologies that will scale across the company’s ecosystem.
- You've excellence in an academic background in a field with statistical, mathematical, computer science or economic focus.
