About the company:
An international company specializing in the gift card industry, offering comprehensive services such as card issuance, distribution, payment processing, exchange, and refilling. The company operates multiple e-commerce platforms dedicated to facilitating the distribution and accessibility of its gift card products.
Work format:
Work processes offer a flexible schedule, averaging 8 hours per day, with occasional meetings scheduled between 5:00 pm and 7:00 pm UTC.
Technical stack:
- AWS.
- Redshift.
- Python, Django.
- Some ML related tools can be picked by engineers.
Team:
7 Data scientists including Lead. Distributed between the USA and India. Tech lead is in India, Manager is in California.
Requirements:
- Proficiency in ML development (feature engineering, algorithm selection, statistics).
- Experience in FinTech, E-Commerce or Retail domains.
- Strong skills in applied mathematics, data analysis and statistics.
- Understanding of modern machine learning techniques and algorithms.
- Proficiency in Python and SQL.
- Experience in building REST API applications.
- Understanding of microservices architecture.
- Experience with model implementation and tuning using popular Machine Learning frameworks such as PyTorch, Keras, TensorFlow.
- English level: Upper-Intermediate or higher.
- Working location: outside Ukraine.
- Online availability overlap with US Pacific time zone till 12:00 PST.
As a plus:
- Experience working with Fraud prevention or Risk modeling.
- Experience with Big Data Processing frameworks (Apache Spark, Hadoop etc).
- Experience with Django framework.
- Experience with AWS cloud.
- Having a Kaggle profile.
Responsibilities:
- Working and professionally communicating with the customer’s team.
- Taking up responsibility for implementation and delivering of ML solutions.
- Participating in requirements gathering & clarification process, proposing optimal architecture solutions, owning the implementation.
- Building and tuning machine learning algorithms for solving business problems in E-commerce and Retail.
- Developing AWS machine learning infrastructure to support and maintain model serving and training.
- Analysis of data for patterns.
- Visualization of data.
- Continuous learning.