Requirements

Expected technologies

Python

Grafana

Snowflake Data Cloud

SQL

AWS

Operating system

Windows

Our requirements

• Degree in Computer Science or related field (Data Science, Big Data, Mathematics, Physics, etc.).
• At least 5 years of proven industry or research experience in data science-related and machine learning projects.
• Strong programming and algorithmics skills in Python, including experience with OOP.
• Advance knowledge of SQL and relational databases, ideally Snowflake.
• Expert knowledge of data analysis, statistics, data mining, machine learning or Big Data processing area.
• Experience with mainstream ML libraries (TensorFlow/PyTorch, Scikit-Learn, Pandas, etc.).
• Familiarity with data structures, CI/CD pipelines, software engineering principles and robust code testing.
• Collaborating in a team using code versioning tools like Git.
• Ability to summarize and present the finding results to business/product stakeholders in an easy-to-follow manner (clear visualizations, results summary in spreadsheets, etc.).
• Good communication skills, ability to work in a Scrum team and leading projects.
• Openness to learn new technologies and adapt to technological stack.

Optional

• Experience in the advertising industry, recommendation systems or real-time bidding (RTB) ecosystem.
• Experience in leading a group of developers in the past, success projects delivery history.
• Familiarity with AWS tools (e.g. Airflow, SageMaker), Snowflake, Redis, Aerospike, ETL or big data tools (PySpark/Snowpark, Docker, Kubernetes, etc.)
• Experience with deploying ML models in production environment.
• Experience with Infrastructure as Code (Terraform), orchestration tools (Airflow), building CI/CD pipelines using Github Actions, and real-time monitoring/alerting frameworks such as Prometheus and Grafana.
• Proven research experience in recommendation systems, or AdTech industry.

Your responsibilities

• Develop, test, deploy, and maintain data, scalable low-latency machine learning products and pipelines supporting ML products considering factors such as the nature of the data, the complexity of the problem, and the available computational resources.
• Validate the model's performance on unseen data, ensuring that it generalizes well and does not overfit the training data. Conduct rigorous testing to identify and address potential issues, such as bias or fairness concerns.
• Design and develop the next generation machine learning platform to support thousands of model training pipelines concurrently and thousands of billions of daily batch predictions.
• Research the latest machine learning platform technologies pushing the boundaries of what is currently possible with ML and keep up-to-date with industry trends and developments.
• Experiment with new ML platforms tailored to our environment and create quick prototypes / proof-of-concepts.
• Streamline model deployment, unit testing, integration testing, and stress testing and ensure engineering quality.
• Support automation of the ML pipeline using CI/CD principles, promoting consistency, reproducibility, and agility.
• Work with Data Scientists to introduce new ML platform features, help streamline the model development process, and reduce the lead time for model production.
• Closely work with different internal ML teams (e.g., Data Scientists and MLOps teams) to improve codebase and product health.
• You will have a chance to technically lead a group of engineers, depending on your experience.