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.

Main goals and responsibilities: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 periodA well-equipped and cozy office supports comfort and productivity across all project stages.Welcoming atmosphere and a friendly corporate culture.

Required: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.Nice to have:Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP);Ability to work with Spark, Airflow.