Description

In this role, you will transform complex data into actionable insights that drive data-driven decision-making. Your daily responsibilities will include analyzing data, improving machine learning models, and ensuring the quality and reliability of data outputs.

Requirements

• •2+ years of experience building production-grade systems
• •Fast, clean, and maintainable code in TypeScript, Node.js, React
• •Understanding of backend fundamentals: Hexagonal Architecture, NestJs/Inversify, Express.js, AWS SQS/SNS, Sockets, PostgreSQL, TypeORM
• •Knowledge of AWS (ECS/EKS), Docker, Kubernetes, Terraform
• •Experience with CI/CD pipelines (GitHub Actions, CircleCI)
• •Pragmatic work approach: prefer simple solutions over abstractions
• •Care for testing and long-term reliability
• •Ownership of built solutions
• •Clear communication with engineers, product managers, and designers
• •Enjoyment of working in a lean, fast-moving team with minimal hierarchy
• •Experience with observability (Prometheus, Grafana, Sentry)
• •Prior work in distributed systems or microservices
• •Experience with AI-assisted development workflows

Tasks

• •Apply statistical and analytical rigor to solve complex business problems
• •Identify patterns and trends in data to support strategic and operational decisions
• •Translate analytical findings into clear, actionable insights for stakeholders
• •Contribute to business dashboards to enhance transparency and metric understanding
• •Analyze existing machine learning structures to identify improvement opportunities
• •Define and create new training datasets based on observed data patterns
• •Support the continuous evolution of models through data-driven experimentation
• •Ensure datasets are representative, scalable, and aligned with business goals
• •Write high-integrity, maintainable code to retrieve, process, and analyze data
• •Examine, validate, and onboard new datasets into the platform
• •Contribute to a modern data platform using engineering best practices
• •Stay updated with modern technologies and data science methodologies
• •Implement mechanisms to measure and monitor the quality of data outputs
• •Validate assumptions, detect anomalies, and ensure consistency across analyses
• •Improve robustness and reliability of analytical pipelines and models
• •Promote reproducibility, documentation, and clarity in all analytical work

Tools & Technologies

Benefits