Data Scientist Executive

The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers.

Requirements

• BSc or MSc degree in a quantitative field such as Computer Engineering, Industrial Engineering, Mathematics, Statistics or related disciplines
• At least 3+ years of hands-on experience in Machine Learning, Data Mining, or Advanced Analytics
• Strong theoretical and practical knowledge of Machine Learning algorithms (regression, classification, clustering, tree-based models, neural networks, etc.)
• Proven experience with data preprocessing, feature engineering, model training, model evaluation and optimization
• Strong proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch is a plus)
• Solid experience with SQL and relational databases
• Familiarity with data visualization tools such as Power BI, Tableau, or equivalent
• Strong knowledge of statistical analysis and experimental design

Nice to Have

• Experience with time series forecasting, recommender systems, NLP or computer vision
• Experience with MLOps, model deployment and automation
• Experience in cloud environments (Azure, AWS, GCP)
• Knowledge of pharmaceutical industry

Job Definition

• Develop, validate, and deploy machine learning and data mining models to solve real business problems
• Perform exploratory data analysis (EDA) and create advanced analytical insights from large datasets
• Design and implement feature engineering pipelines and model optimization strategies
• Work closely with business teams to translate business problems into analytical solutions
• Build predictive, classification and segmentation models
• Monitor model performance and continuously improve model accuracy
• Write efficient SQL queries to extract and prepare data from enterprise data sources
• Develop scalable analytics solutions using Python-based data science frameworks
• Document methodologies and communicate results clearly to both technical and non-technical stakeholders
• Contribute to the development of advanced analytics roadmap