Python Engineer (Data Science)
Paying bills sucks. It sucks even more for about a third of Americans living paycheck to paycheck, and struggling to pay bills on time. About 100 million Americans are at risk of paying overdraft fees if they auto-pay their bills, and are forced to manually log into and pay up to 10 biller accounts each month. Why are so many people wasting their time and energy manually paying bills to avoid overdraft fees? Because they are not using Gerald!
Gerald is on a mission to eliminate stress about paying bills. We are transforming bill payments by providing consumers with an application for linking and automatically paying their household bills while offering overdraft and late fee protection for all their biller accounts. Our app tracks and pays bills so our users don’t have to. We offer banking services; direct deposits, credit building, cash advance and rewards to simplify bill payments for both billers accounts and consumers.
Our platform combines fintech, payments, and big data to create an empowering and rewarding user experience for making timely bill payments.
Skills: Node.js, Python, React Native, TypeScript, PostgreSQL, Amazon Web Services (AWS)
About This Role The role requires a mix of strong technical expertise, engineering best practices, business engagement, hands-on architecture, projects delivery, and cross-team collaboration. In addition, the candidate ought to have passion for inspiring and mentoring juniors engineers with varying levels of experience.
In particular, as a Python engineer in the Data Science team, you should possess an in-depth knowledge of object-relational mapping, experience with server-side logic, and knowledge of Python programming/scripting. You should have good understanding of machine learning models and data science concepts. You also will be responsible for writing and testing scalable code, developing back-end components, and integrating user-facing elements in collaboration with other developers.
If you understand how to balance speed, long-term scalability and performance, want to contribute ideas and be part of a small team building a lot of things from scratch, this may be the opportunity for you.
Tech Stack We Use React Native, Node.js (Express), TypeScript, Python, PostgreSQL with AWS as the primary cloud provider
How You Will Contribute
- Build services that are part of our Gerald's decision engine—the core of Gerald's product
- Develop statistical machine learning models and data pipelines to serve Gerald's decision engine
- Develop backend components to enhance performance, receptiveness, and server-side logic and constantly improve the platform
- Create RESTful APIs development using Python Flask or Django REST framework
- Understand, analyze, and implement business needs, feature modification requests and conversion into software components
- Demonstrate good DevOps practices
- Write efficient, reusable, testable, and scalable code
- Find scalable solutions to challenging technical problems
- Write technical documentation, lead code reviews and pair programming sessions
- Keep current with industry trends and developments that can be applied to company needs
- Be a technical leader within the team you work with and within Gerald in general
What Will Make You Successful In This Role
- 2-4 years of experience as a Python Developer
- Experience using various Python libraries, like Pandas, SciPy, and tools, such as Jupyter
- Familiarity with Amazon Web Services (AWS) such EC2, S3, RDS, Athena, AWS Glue, etc.
- Proficient understanding of code versioning tools such as GitHub
- Knowledge of Docker and CI/CD is an additional advantage
- Strong experience working with any SQL databases such as PostgreSQL is required. NoSQL database (MongoDB) experience will be an advantage
- Experience in developing data engineering pipelines & ETL jobs
- Experience in developing AI/ML and data science modules
- Bachelor's degree in Computer Science, Computer Engineering, or related field
Soft-Skills That Will Come Handy
- Excellent written and verbal communication skills
- Strong attention to detail
- Head for problem-solving
- Having a flexible working style
- Being a team player
- Comfortable with working unsupervised
With every single customer that we onboard, it means we are dealing with millions of data records. The challenge is to scale up the product to handle so much data and process it efficiently, while taking all measures to ensure the security of the data. The goal is to make our system learn as quickly as possible so that it can give very accurate predictions when making suggestions related to consumers.