Middle Data Scientist

We are currently seeking a skilled and experienced Data Scientist to join our team. As a Data Scientist, you will be responsible for experiments with datasets searching for useful insights and features, maintaining data pipelines and training machine learning algorithms, and visualizing results.

• Be a proactive team worker;

• Collaborate with the team to implement new features to support the growing data needs;

• Build, maintain, and deploy DS and ML pipelines, including data retrieval, preprocessing, feature engineering, training, and inferencing the models, analyzing and visualizing the results;

• Build, maintain and deploy LLM-powered systems and agent pipelines, including prompt engineering, tool and API integration, memory and context management, inference orchestration, and evaluation of model outputs;

• Share knowledge with other teams on various data science or project-related topics;

• Collaborate with the team to decide on which tools and strategies to use within specific scenarios.

What We Offer:

• Long-term career stability with a competitive salary paid in USD.

• Conditions for steady career development.

• Development supported by dedicated mentors and a variety of programs focused on expertise and innovation.

• Private medical insurance provided after successful completion of the probationary period

• A well-equipped and cozy office supports comfort and productivity across all project stages.

• Welcoming atmosphere and a friendly corporate culture.

Requirements

• English level upper intermediate+;

• Good mathematical and statistical background, tensor calculus;

• Good knowledge of databases such as Postgres, MongoDB, and SQL;

• Python language including practical experience with Scikit-learn, Numpy, Pandas, and Matplot libraries;

• Experience with gradient boosting algorithms (XGBoost or LightGBM);

• Familiarity with LLM orchestration tools (e.g., LangChain, LangGraph);

• Experience using or integrating with cloud LLM APIs (e.g., OpenAI, Claude, Amazon Bedrock, Vertex AI);

• Hands-on experience with Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., pgvector, FAISS, Weaviate, Pinecone);

• Practical experience with prompt engineering techniques, agentic tools and workflows;

• At least 1 framework to build and train neural networks (TensorFlow/Keras, PyTorch), a good understanding of neural networks architectures, and how-tos;

• Commercial experience with classic machine learning and deep learning, including NLP and CV models like Bert, Resnet;

• Practical skills in building en-to-end ML training pipelines (data load, preprocess, train, inference), as well as GitHub / GitLab CI/CD flows;

• Docker or Kubernetes platform;

• Work experience as a Data Scientist more than 2 years.

• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP);

• Ability to work with Spark, Airflow.