Data Scientist

Requirements:

• 5+ experience in applying data science and machine learning techniques
• Python programming using classic scientific stack (numpy, pandas, scipy, scikit-learn, pytorch, lightgbm, matplotlib or any other visualization library)
• Understanding of classic machine learning algorithms and theory (supervised/unsupervised learning techniques, metrics and visualizations for interpretation of model performance, time-series analysis, anomaly detections, recommendation models, hyperparameter optimization)
• NLP (NLP techniques, GenAI, LLM finetuning)
• Statistics and optimization basic understanding
• Data manipulation and cleaning skills (fill missing entries / filter dataframes / apply certain changes to columns)
• Prior experience with Python web frameworks would be great, but not critical (FastAPI, Flask or anything else)
• Dockerization technologies
• Cloud solutions (any of AWS, GCP, Azure)
• Understanding of prompt engineering or experience with building scripts or applications around LLMs
• Proficient with SQL (Postgres, Snowflake), NoSQL (ElasticSearch, Mongo(optional))
• Monitoring services
   
• Responsibilities:
  Work on time series forecasting problems with real, complex data. Implement features, interpret their importance, train models and incorporate them in the production pipeline.
• Work on LLM-based solutions, which use techniques like Contextual RAG (Retrieval Augmented Generation), LLM as Judge, semantic search, chatbot services, etc.
• Create anomaly detection models and clustering methods for non-stationary, “dirty”, semi-organized data