Data Science (Python+ Statistic)

Data Science (Python + Statistics.)
Key Responsibilities Designing and Executing Rigorous Statistical Tests: This responsibility entails the foundational work of ensuring that any observed relationship between variables is not merely due to chance. The specialist will design experiments and tests to establish the likelihood that an outcome is genuinely caused by the factors under study, rather than random variability. Proactive involvement in experimental design is essential, moving beyond mere post-hoc analysis. Performing Hypothesis Testing and Statistical Inference: The specialist will be responsible for defining and testing null and alternative hypotheses to determine statistical significance. Applying Advanced Statistical Methods for Data and Model Analysis: This encompasses a broad range of techniques vital for deeply analyzing datasets and model outputs. These include probability theory, which quantifies uncertainty and aids in prediction. Various sampling methods (e.g., random, stratified, cluster) are crucial for selecting representative subsets of data, allowing for reliable inferences about larger populations. Evaluating and Recommending Appropriate ML Algorithms: The specialist will assess the suitability of various ML algorithms, including supervised learning tasks like classification, regression, and forecasting, as well as unsupervised learning methods such as clustering and dimensionality reduction, and reinforcement learning. Conducting In-depth Analysis of Algorithmic Soundness and Applicability: This involves scrutinizing the theoretical underpinnings of selected algorithms, their assumptions, and their appropriateness for the specific problem and dataset. Assessing Model Performance Metrics: The specialist will evaluate models based on a variety of metrics relevant to the task. For classification tasks, this includes accuracy, precision, recall, and F1- score; for regression, metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are used. Required Qualifications Masters in a quantitative field such as Computer Science, Data Science, Engineering Statistics, Mathematics, or Machine Learning. Experience Minimum of 5+ years of progressive experience in machine learning validation, model risk management, data science, or a closely related role with a strong focus on statistical rigor and algorithmic assessment. Proven track record in successfully validating and deploying AI/ML-based solutions to solve complex business Experience with SQL for querying and managing databases. Familiarity with C++ and/or Java for performance-critical applications or integration. The ability to work across different parts of the ML stack, from data querying to model deployment and potentially low-level optimization. Machine Learning Frameworks & Tools: Experience with common ML frameworks such as TensorFlow and PyTorch. Data Analysis & Statistical Tools: Proficiency in data analysis and statistical tools, including Python/R libraries like NumPy, SciPy, Pandas, Matplotlib, and Seaborn. Data Visualization Tools: Experience with data visualization software like Looker, Looker Studio to present findings effectively. Deep understanding of machine learning algorithms, their underlying principles, and performance metrics. Strong knowledge of statistical analysis, modeling techniques, and hypothesis testing. Familiarity with advanced statistical modeling techniques such as Bayesian Inference and Variational Inference, and their integration with AI models. The mention of these advanced techniques suggests a need for cutting-edge statistical knowledge that goes beyond traditional frequentist methods. This indicates a role that is expected to contribute to the advancement of validation methodologies, not just their application.