Requirements:

• Self-reliance and the ability to work without a technical task;
• Confident knowledge of python (3.9+) and experience with asyncio;
• Experience in developing anti-fraud systems;
• In-depth knowledge of system design;
• Experience with Redis;
• Understanding of classical algorithms and data structures;
• Experience with databases (queries, migrations, optimization, profiling);
• 4+ years of experience in developing stable and scalable web services and APIs (REST, JSON-RPC, gRPC).

Nice to have:

• Experience with highly loaded systems;
• Experience with ClickHouse;
• Ability to visualize data in many ways;
• Experience finding anomalies and suspicious patterns in data.

Stack:

• Python (asyncio, FastAPI, Faust), SQL;
• Redis Stack, ClickHouse, PostgreSQL, MongoDB;
• Kafka;
• Docker, Kubernetes;
• Prometheus, Grafana, Sentry, Kibana.

Tasks:

• Search for anomalies in the data. It will be necessary to analyze user behavior, find abnormal patterns, and automate their identification;
• Developing an anti-fraud system. We will need to develop an infrastructure of checks and algorithms to find single violations and fraudulent farms;
• Development of a monitoring and alerting system. We need to develop a system of monitoring and alerting on fraudulent actions.

Benefits and Perks:

• GPU/CPU servers in the cloud;
• Top-notch equipment and all necessary software;
• Office within walking distance of Dobryninskaya / Serpukhovskaya metro stations;
• Possibility of remote work;
• Option grant available;
• Flexible schedule.

About the team:

We are an R&D team that deals with everything related to data and machine learning. Our team consists of 6 people, including Data Analysts, ML Engineers, and Software Engineers - a cross-functional team about data, ML, and engineering. We are responsible for the complete cycle of our developments. That is, we build models ourselves, wrap them into services and deploy them into production. We monitor our services ourselves. We are responsible for the fault tolerance of the system.

Our team handles the following tasks: recommendation system, content automoderation, anti-fraud, product and marketing analytics, video generation.

We strive to grow everyone within the team to be full-stack. That is, we teach data scientists and data analysts to write production code. Engineers learn to train models. ML engineers understand how the product and product analytics work.

Our team does not have a project manager or a system analyst who would write us technical specifications. We understand the priorities of the business ourselves, synchronize with the business. We formulate tasks ourselves, decompose them, and distribute them within the team. Often we come to the business with new solutions and ideas ourselves.

We work in two-week sprints. Stand-up every morning. Demo every Friday at the end of the sprint. Always retrospective after the demo. Every 4 weeks there is a 1to1 with the team lead for each team member. Every 6 months, everyone has a Performance Review. We use Jira, but without fanaticism.