رستم | Rostam

کامپیوتر، فناوری اطلاعات و اینترنت ۲ - ۱۰ نفر

فرصت‌های شغلی ۳

استخدام Senior Data Scientist (دورکاری)

دسته‌بندی شغلی

وب،‌ برنامه‌نویسی و نرم‌افزار
موقعیت مکانی

تهران ، تهران
نوع همکاری

تمام وقت دورکاری
حداقل سابقه کار

بیش از شش سال
حقوق

از ۱۳۰,۰۰۰,۰۰۰ تومان

شرح موقعیت شغلی

About the Role:
We are seeking an experienced Data Scientist / AI Engineer with strong software engineering fundamentals to join our growing team. In this role, you will design, build, and deploy production-grade machine learning systems—spanning time-series forecasting, anomaly detection, classification, and NLP—that drive core decision-making across our platform.

This is not a research-only position. You will own models end-to-end: from exploratory analysis and experimentation through to production deployment, monitoring, and iteration. You will work within a collaborative engineering culture that values clean code, testability, and operational excellence.

Responsibilities:

Research, design, and implement ML models for time-series forecasting, classification, anomaly detection, and NLP tasks.
Develop and iterate on deep learning models using PyTorch, including transformer-based architectures for sequential and textual data.
Conduct exploratory data analysis (EDA) and feature engineering on structured and unstructured datasets.
Design and run experiments systematically, leveraging MLflow for experiment tracking, model versioning, and reproducibility.
Optimize model training and inference for performance, including GPU memory management and batch processing strategies.
Stay current with state-of-the-art research and evaluate new techniques for applicability to our domain.
Write clean, testable, and maintainable code following Clean Architecture and modern Python best practices.
Implement asynchronous and functional programming patterns where appropriate to optimize performance.
Develop and maintain automated tests using pytest, following Test-Driven Development (TDD) practices.
Contribute to the ML platform codebase with proper dependency injection and testable design patterns.
Automate CI/CD pipelines using GitHub Actions and maintain a healthy monorepo structure.
Support Docker-based development, including multi-container setups and container orchestration.
Collaborate with data engineers to define data requirements, build training datasets, and ensure data quality.
Deploy models into production within containerized cloud environments and monitor their performance.
Monitor, troubleshoot, and optimize cloud-based applications and data workflows.

Required Skills & Qualifications

Strong foundation in supervised and unsupervised learning, statistical modeling, and probability theory.
Hands-on experience with PyTorch for building and training deep learning models (CNNs, RNNs, Transformers).
Experience with time-series analysis and forecasting methods (classical and deep learning-based).
Familiarity with NLP techniques including text classification, named entity recognition, and transformer-based language models.
Experience with scikit-learn for classical ML workflows and model evaluation.
Proficiency in pandas, NumPy, and data visualization libraries (matplotlib, seaborn, plotly).
Strong skills in feature engineering, data preprocessing, and handling imbalanced or noisy datasets.
Experience with experiment tracking and model registry tools (MLflow preferred).
Understanding of hyperparameter tuning strategies and model selection best practices.
Strong Python skills with experience in OOP, functional programming, dataclasses, dependency injection, and async programming.
Familiarity with FastAPI or other modern Python web frameworks.
Experience implementing Clean Architecture and working with monorepo structures.
Strong experience with version control (Git, GitHub) and CI/CD pipelines (GitHub Actions).
Experience with TDD and automated testing using pytest.
Azure: Function Apps, Container Apps, Container Jobs, EventGrid, Blob Storage.
AWS: Lambda, EC2, ECS, EventBridge, S3.
PostgreSQL, MongoDB, Redis.
SQLAlchemy, Alembic for migrations and ORM-based data modeling.
Docker and Docker Compose experience.
Familiarity with GPU-based training workflows and resource optimization.
Understanding of event-driven architectures for triggering training and prediction jobs.

Soft Skills:

Ability to communicate complex modeling decisions clearly to both technical and non-technical stakeholders.
Strong analytical thinking and meticulous attention to detail.
Comfortable working in a fast-paced startup environment with evolving priorities.
Self-driven with the ability to own projects from research through production deployment.

Preferred Qualifications:

Experience with complex, data-rich industry domains (e.g., logistics, finance, operations).
Familiarity with graph-based models or knowledge graphs for network analysis.
Experience with LLMs and retrieval-augmented generation (RAG) pipelines.
Knowledge of causal inference methods.
Experience with distributed training or model parallelism for large-scale models.

Education (Preferred):

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Statistics, Data Science, Applied Mathematics, or a related quantitative field.
Strong foundation in linear algebra, calculus, probability, optimization, algorithms, data structures, databases, and software engineering principles.

Note:

Please submit your resume in English only.

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