Mid-level Data Scientist

Description

Description: * Proficiency in Python and libraries such as pandas, numpy, scikit\-learn, matplotlib/seaborn. * Good programming practices: version control with Git, modularization, docstrings, basic testing. * Experience and hands-on practice with Docker. * Experience in SQL and manipulation of relational databases (PostgreSQL, MySQL) and non-relational databases (MongoDB, Elastic). * Knowledge in ETL/ELT and data architecture. * Experience building and evaluating supervised and unsupervised Machine Learning models. * Skills in data pre\-processing: handling missing values, normalization, encoding, scaling, etc. * Experience with hyperparameter tuning and interpretable ML tools. * Knowledge of cloud services focused on data and ML (AWS S3, SageMaker, Lambda, etc.). * Basic understanding of model deployment as API or integration into existing pipelines. * Familiarity with GenAI and use of LLM APIs. * Strong communication skills and ability to translate business problems into analytical problems. To be a Data Scientist at A3Data, we are looking for someone who will: * Apply solid fundamentals of descriptive and inferential statistics for data analysis and interpretation, including probabilistic modeling. * Use linear algebra and basic calculus to understand and develop Machine Learning models. * Collect, clean, manipulate, and integrate data from various sources (APIs, files, data lakes, relational and non-relational databases). * Develop, train, and evaluate supervised and unsupervised models (regression, classification, clustering). * Perform feature selection and engineering, as well as hyperparameter tuning (Grid Search, Random Search, Bayesian Optimization). * Implement model evaluation metrics (RMSE, MAE, F1\-score, ROC\-AUC) and apply interpretable ML techniques (SHAP, LIME). * Create visualizations, interfaces, and dashboards using Streamlit, Power BI, Tableau, or equivalent tools (e.g., Plotly), presenting results and functionalities clearly and business-oriented. * Deploy models via APIs (Flask/FastAPI) and integrate solutions into existing pipelines. * Work with cloud services (AWS, Azure, or GCP) for storage, processing, and model deployment. * Prototype and integrate Generative AI solutions (LLMs, AWS Bedrock, OpenAI APIs, Azure OpenAI, etc.) for business applications. * Collaborate with data engineers, PMs, and stakeholders to translate business problems into analytical solutions. 2509130202531447510