Data Scientist

Responsibilities: • Develops and refines machine learning models to solve specific business problems (Model Training) • Conducts feature engineering to enhance model performance and represent data optimally • Performs hyperparameter tuning to optimize model performance • Designs and implements ML pipelines that sequence different steps for optimal results (e.g., model training, evaluation, data transformation) • Selects appropriate algorithms based on problem requirements, data characteristics, and performance criteria • Analyzes data to uncover trends, patterns, and insights • Considers and accounts for data drift over time to ensure models remain effective in production • Conducts prompt engineering to improve performance of models requiring natural language inputs • Ensures quality through model evaluation and rigorous testing of results • Estimates costs for ML-related tasks, including project offers and change requests • Collaborates with business stakeholders to deeply understand the business case behind the ML model and its expected outcomes Requirements: 1. Strong knowledge of machine learning algorithms, hyperparameter tuning, and model evaluation 2. Experience with prompt engineering and natural language processing (NLP) tasks 3. Proficiency in Python 4. Familiarity with ML libraries such as TensorFlow, Scikit-learn, and PyTorch 5. Knowledge of data analysis techniques, feature engineering, and data preparation 6. Understanding of ML pipelines and workflow automation 7. Ability to detect and address data drift in production environments 8. Strong problem-solving and analytical thinking skills 9. Experience working with stakeholders to understand and refine business cases 10. Familiarity with cost estimation and budget management for ML projects 11. Excellent communication skills to convey technical insights in business terms 12. Continuous learning mindset, staying updated with the latest in AI/ML technologies 13. Strong problem-solving and analytical thinking skills. 14. Experience working with stakeholders to understand and refine business cases. 15. Continuous learning mindset, staying updated with the latest in AI/ML technologies. 16. Primary Skills: o Core Libraries: Python 3.11+, pandas, NumPy, scikit-learn o Deep Learning: TensorFlow, Keras, PyTorch o Natural Language Processing: (Azure) OpenAI, Prompt Engineering, transformers, Langchain o Development & Deployment: Git, Docker o Data Visualization & Application: Streamlit, Matplotlib o Azure: Azure Cognitive Services, Azure Databricks Secondary Skills: o Azure Databases & Data Services: Azure Cosmos DB, Azure Data Factory