Advanced Data Scientist

Job Description

Requirements- Bachelor's or Master's degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science). -1-2 years of hands-on experience in data science or related fields. -Strong statistical background with proficiency in R and Python. -Familiarity with Mixed Integer Programming and Optimization concepts is preferred. Responsibilities- -Experience with ETL development and data exploration capabilities. Knowledge of Bayesian methods, frequentist statistics, multivariate analysis, and handling outliers. -Develop and implement statistical models to extract insights from complex datasets. -Utilize machine learning techniques for both supervised and unsupervised learning tasks. -Collaborate with stakeholders to define data requirements and ensure the relevance of analyses. -Perform data cleaning, transformation, and analysis using ETL methodologies. -Communicate findings through clear visualizations and reports, making data accessible to non-technical teams. Key Skills- -Experience with ETL development and data exploration capabilities. Knowledge of Bayesian methods, frequentist statistics, multivariate analysis, and handling outliers. -Understanding of AI/ML principles, especially in predictive analytics. -Prior experience in the pharmaceutical industry (commercial or R&D) is highly desirable. -Develop and implement statistical models to extract insights from complex datasets. -Utilize machine learning techniques for both supervised and unsupervised learning tasks. -Collaborate with stakeholders to define data requirements and ensure the relevance of analyses. -Perform data cleaning, transformation, and analysis using ETL methodologies. Communicate findings through clear visualizations and reports, making data accessible to non-technical teams. -Advanced R and Python programming, especially with libraries like NumPy, Pandas, Seaborn, and Plotly. -Experience with machine learning models such as RF, XGBoost, regression, and kNN. -Knowledge of optimizations and Mixed Integer Programming principles. -Strong understanding of ETL processes and data exploration techniques.