Your responsibilities

Prepare and validate structured datasets for machine learning tasks, including cleaning, transformation, encoding, scaling, and missing-value handling;
Conduct exploratory data analysis to identify patterns, data quality issues, and opportunities for modeling;
Build, test, and compare ML models for tasks such as regression, classification, clustering, dimensionality reduction, and anomaly detection;
Develop and select features with guidance from senior engineers;
Run experiments, tune model parameters, and evaluate results using appropriate validation approaches and metrics;
Help maintain reproducible ML pipelines, notebooks, experiment tracking, and technical documentation;
Write and maintain automated tests for data and model-related code;
Support the delivery of models through REST APIs or batch-processing workflows;
Document model assumptions, results, limitations, and technical decisions;
Work closely with senior engineers, incorporate feedback, and gradually take ownership of more complex tasks;

Our requirements

1+ year of hands-on Python development experience, including experience with pandas, NumPy, scikit-learn, and Jupyter;
A strong foundation in statistics, probability, and linear algebra, plus exploratory data analysis skills;
An understanding of supervised and unsupervised learning, and practical experience with regression, classification, clustering, dimensionality reduction, and anomaly detection;
Experience preparing structured datasets, including cleaning, transformation, encoding, scaling, and missing-value handling, plus practical feature engineering and feature-selection skills;
An understanding of train/validation/test splits, cross-validation, data leakage, overfitting, and regularization; the ability to select appropriate evaluation metrics, compare models, and perform error analysis;
Familiarity with hyperparameter tuning and reproducible ML pipelines;
SQL skills for data extraction and analysis;
The ability to expose models through REST APIs or batch-processing workflows;
Familiarity with Git, automated testing, Docker, and basic model monitoring;
The ability to explain model behavior, assumptions, limitations, and results;
English at B2 level or higher;

XGBoost, time-series analysis, recommendation systems, MLflow, model interpretability, cloud services, production ML monitoring, and GenAI, LLM, RAG, prompt-engineering, or agent-development experience;

Resources and opportunities for self-education in technical and non-technical areas;
Access to internal conferences and meetups for knowledge sharing and learning from industry experts;
Medical insurance;