Senior Data Scientist - Statistical Modeling

<p>Role & responsibilities :<br/><br/>- Translate real-world problems into well-structured mathematical/statistical models<br/><br/>- Design and implement algorithms using linear algebra, optimization, and probability theory<br/><br/>- Develop predictive and classification models using ML techniques (e.g., regression, trees, neural networks, clustering)<br/><br/>- Conduct exploratory data analysis and feature engineering on large datasets<br/><br/>- Use hypothesis testing, inferential statistics, and confidence intervals to validate results<br/><br/>- Collaborate with product and engineering teams to integrate models into production pipelines<br/><br/>Required Skills & Competencies :<br/><br/>1.

Mathematics & Statistics :<br/><br/>- Strong understanding of Linear Algebra, Multivariable Calculus, Probability & Statistics<br/><br/>- Hands-on experience with statistical inference, hypothesis testing, and confidence intervals<br/><br/>2.

Machine Learning & Algorithms :<br/><br/>- Practical knowledge of supervised and unsupervised learning<br/><br/>- Comfortable with algorithms like regression, SVMs, decision trees, clustering<br/><br/>- Understanding of model performance evaluation (F1-score, AUC-ROC, cross-validation, etc.)<br/><br/>3.

Technical Skills :<br/><br/>- Proficient in Python (NumPy, Pandas, Scikit-learn, Statsmodels)<br/><br/>- Experience with ML libraries like TensorFlow, PyTorch<br/><br/>- Exposure to Jupyter, Matplotlib/Seaborn, and Git<br/><br/>4.

Problem-Solving Mindset :<br/><br/>- Strong analytical and critical thinking<br/><br/>- Ability to convert vague requirements into well-defined problems<br/><br/>- Comfortable dealing with incomplete, noisy, or unstructured data<br/><br/>Good to have :<br/><br/>1.

Exposure to graph theory, time-series analysis, or numerical methods<br/><br/>2.

Experience with real-time data pipelines, GraphQL, SQL, or NoSQL<br/><br/>3.

Experience in scientific computing or simulation modeling</p> (ref:hirist.tech)