Middle Data Scientist (ML)

#lookfor #outsource #outstaff #remote #DataScientist #MachineLearning #Python #SQL #PySpark #ML #DeepLearning #ScikitLearn

We are looking for a Middle Data Scientist to join our data team on a full-time remote basis.

The specialist will develop, validate, and deploy machine learning models, building end-to-end ML pipelines from data exploration and feature engineering to production deployment.

Key responsibilities:
• Develop, train, and validate ML models for classification, regression, forecasting, and clustering.
• Perform exploratory data analysis (EDA) and design feature engineering pipelines.
• Build and maintain end-to-end ML workflows: preprocessing, training, evaluation, and deployment.
• Work with structured and unstructured data using SQL, Python, and PySpark.
• Implement and tune models using Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, or PyTorch.
• Evaluate model performance with metrics, cross-validation, and A/B testing.
• Deploy models to production and monitor performance over time.
• Collaborate with data engineers on pipelines and feature stores.
• Translate business requirements into data science tasks and present results to stakeholders.
• Document model architectures, experiments, and methodologies.

Requirements:
• 3+ years of commercial experience in Data Science or Machine Learning.
• Strong Python proficiency for data analysis, modeling, and automation.
• Expert-level SQL skills for data extraction, aggregation, and complex querying.
• Hands-on experience with PySpark for distributed data processing.
• Solid understanding of classical ML algorithms and practical applications.
• Experience with Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, or PyTorch.
• Strong foundation in statistics, probability, and mathematical optimization.
• Experience with feature engineering, dimensionality reduction, and model selection.
• Understanding of MLOps concepts: model versioning, experiment tracking, and deployment.
• Familiarity with MLflow, Kubeflow, DVC, or Weights & Biases.
• Experience with cloud ML platforms (AWS SageMaker, Azure ML, or Vertex AI).
• Knowledge of A/B testing, causal inference, and data visualization tools.
• Strong analytical thinking and ability to translate business problems into data-driven solutions.
• English: B2 or higher (written and spoken).

Nice to have:
• Experience with NLP (spaCy, NLTK, Hugging Face) or computer vision (OpenCV, Detectron2).
• Knowledge of Bayesian methods, time series forecasting, or graph-based ML.
• Contributions to Kaggle competitions, open-source ML projects, or research publications.

Location: Remote, worldwide
Restrictions: Candidates from Egypt, India, Pakistan, and Afghanistan are not considered
English: B2+
Format: Full-time, outsource, outstaff
Contact: Показать контакты