01Overview
Role & Responsibilities Translate real-world problems into well-structured mathematical/statistical models Design and implement algorithms using linear algebra, optimization, and probability theory Develop predictive and classification models using ML techniques (e.g., regression, trees, neural networks, clustering) Conduct exploratory data analysis and feature engineering on large datasets Use hypothesis testing, inferential statistics, and confidence intervals to validate results Collaborate with product and engineering teams to integrate models into production pipelinesRequired Skills & Competencies Mathematics & Statistics : Strong understanding of Linear Algebra, Multivariable Calculus, Probability & Statistics Hands-on experience with statistical inference, hypothesis testing, and confidence intervals Machine Learning & Algorithms : Practical knowledge of supervised and unsupervised learning Comfortable with algorithms like regression, SVMs, decision trees, clustering Understanding of model performance evaluation (F1-score, AUC-ROC, cross-validation, etc.) Technical Skills : Proficient in Python (NumPy, Pandas, Scikit-learn, Statsmodels) Experience with ML libraries like TensorFlow, PyTorch Exposure to Jupyter, Matplotlib/Seaborn, and Git Problem-Solving Mindset : Strong analytical and critical thinking Ability to convert vague requirements into well-defined problems Comfortable dealing with incomplete, noisy, or unstructured dataGood To Have Exposure to graph theory, time-series analysis, or numerical methods Experience with real-time data pipelines, GraphQL, SQL, or NoSQL Experience in scientific computing or simulation modeling(ref:hirist.tech) .
