Bilingual Data Scientist

EPAM NEORIS is a Digital accelerator that helps companies enter the future, having 20 years of experience as Digital Partners of some of the largest companies in the world. We have more than 4,000 professionals in 11 countries, with our multicultural startup culture where we cultivate innovation, continuous learning to create high-value solutions for our clients.

We are looking for a Bilingual Data Scientist :

• 5+ years of experience in Data Science, Machine Learning, or AI-related roles.
• Strong proficiency in Python and experience with SQL for large-scale data analysis.
• Hands-on experience building and deploying ML models using frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Practical experience developing and deploying computer vision models (e.g., object detection, image classification, segmentation).
• Hands-on experience with LLMs, prompt engineering, embeddings, and RAG-based architectures.
• Familiarity with data processing or streaming pipelines (e.g., Apache Beam, Google Cloud Dataflow, Spark or similar frameworks).
• Experience working with REST APIs and integrating models into production systems.
• Strong analytical and communication skills with English proficiency (B2+ or higher).

• Experience with cloud platforms (GCP, AWS, or Azure) and MLOps tools (Docker, Kubernetes, MLflow).
• Familiarity with GenAI frameworks (LangChain, LlamaIndex, ADK).
• Experience working in Agile environments and using version control (Git).
• Exposure to real-time or streaming data pipelines.
• Experience mentoring junior team members or leading technical initiatives.

• →End-to-End Model Development: Design, develop, and deploy machine learning models, including data preprocessing, feature engineering, training, validation, and performance optimization.
• →Advanced Analytics: Apply statistical and machine learning techniques (regression, classification, clustering, anomaly detection) to solve complex business challenges.
• →Computer Vision Solutions: Develop and implement computer vision models (e.g., object detection, classification, segmentation) for real-world use cases.
• →Generative AI & LLMs: Design and build GenAI applications using LLMs, prompt engineering, embeddings, and RAG pipelines.
• →System Integration: Integrate AI/ML models into applications using APIs and backend services.
• →Data Pipelines & MLOps: Collaborate with engineering teams to build scalable data pipelines and deploy models using modern MLOps practices.
• →Code & Best Practices: Write high-quality, modular, and production-ready code using Python and SQL.
• →Stakeholder Communication: Translate complex technical findings into actionable insights through clear visualizations and reporting.
• →Collaboration & Leadership: Work closely with data engineers, developers, and business stakeholders, and mentor junior team members.