> DOU

Machine Learning Engineer

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pythonpandaspolarscatboostxgboostlightgbmsqlclaude codegithub copilotcursorstatisticsmachine learningprompt engineeringragllmembeddingsai agentsapi

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> full description

Role Overview

We are looking for a Machine Learning Engineer / Data Scientist who can turn data into working products — from exploring data and building ML models to bringing them into production.

You will work on real business problems, take ownership of the quality of your solutions throughout the entire lifecycle, and actively use AI-driven development tools in your day-to-day work.

More information about us — [meet INSART]

Role Responsibilities

Develop, train, and validate ML models for applied business problems, including forecasting, classification, ranking, recommendations, anomaly detection, and risk assessment.
Perform exploratory data analysis (EDA), formulate and validate hypotheses, and design appropriate metrics and experiments.
Build feature engineering and data preparation pipelines using pandas and Polars.
Work with gradient boosting algorithms such as CatBoost, XGBoost, and LightGBM, including hyperparameter tuning, cross-validation, overfitting prevention, and data leakage control.
Take ML models from prototype to production, including packaging, deployment, quality and data-drift monitoring, and retraining.
Build and integrate AI/ML solutions, including LLM-powered applications, RAG, embeddings, and agent-based solutions where relevant.
Use AI-driven development approaches and tools such as Claude, Copilot, Cursor, or similar solutions for coding, code review, testing, and documentation while critically validating the generated output.
Develop APIs and services around AI/ML components when required.
Evaluate AI/ML solutions using relevant quality, performance, latency, and cost metrics.
Analyze client data and business processes to identify opportunities for AI/ML implementation.
Communicate technical findings and model results to business and cross-functional teams, clearly explaining how the model works, its limitations, and its business impact.
Take ownership of assigned AI/ML components from requirements and development through production and continuous improvement.
Participate in code reviews and contribute to engineering best practices.

Role Requirements

Strong Python skills and experience writing clean, structured, and tested code.
Hands-on experience with pandas and Polars, including understanding their differences and optimizing data processing for large datasets.
Practical experience with CatBoost, XGBoost, and/or LightGBM, with an understanding of when to use different approaches.
Strong foundation in statistics, probability, and ML fundamentals, including metrics, model validation, bias/variance, and handling imbalanced data.
Strong SQL skills.
Proven experience taking ML models from prototype to a real production product.
Practical experience with AI-driven development, using tools such as Claude, Copilot, Cursor, or similar solutions.
Ability to effectively prompt AI tools, review generated code, and maintain control over its quality.
Experience with Git, teamwork, and code review.
Ability to independently own technical tasks and communicate effectively with technical and non-technical stakeholders.
Strong communication skills: English B2+

Nice to Have

Experience with scikit-learn, Optuna, SHAP, and model interpretability tools.
MLOps experience with MLflow, Docker, CI/CD, Airflow, Prefect, or similar tools.
Experience with cloud platforms such as GCP, AWS, or Azure.
Experience with data warehouses such as BigQuery or Snowflake.
Experience with recommendation systems, time series, uplift modeling, LTV/churn, or similar applied ML problems.
Experience with LLMs, RAG, embeddings, and agent-based solutions.
Experience with A/B testing and causal analysis.
Experience with GraphRAG, MCP, or AI orchestration frameworks.

Daily schedule

Our company supports work-life balance and allows you to tailor your schedule, which consists of 8 working hours and time for lunch. Shared business hours for most of our teams are 12 pm — 7 pm Kyiv time.

Interview Process