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 pipelines Required 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 data Good 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)
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 pipelines Required 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 data Good 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)
As a Data Scientist at our company, you will be responsible for translating real-world problems into well-structured mathematical/statistical models. You will design and implement algorithms using linear algebra, optimization, and probability theory. Your role will involve developing predictive and classification models using ML techniques such as regression, trees, neural networks, and clustering. Additionally, you will conduct exploratory data analysis and feature engineering on large datasets. You will use hypothesis testing, inferential statistics, and confidence intervals to validate results and collaborate with product and engineering teams to integrate models into production pipelines. Key Responsibilities: - Translate real-world problems into mathematical/statistical models - Design and implement algorithms using linear algebra, optimization, and probability theory - Develop predictive and classification models using ML techniques - Conduct exploratory data analysis and feature engineering - Collaborate with product and engineering teams Qualifications Required: - Strong understanding of Linear Algebra, Multivariable Calculus, Probability & Statistics - Hands-on experience with statistical inference, hypothesis testing, and confidence intervals - Practical knowledge of supervised and unsupervised learning - Comfortable with algorithms like regression, SVMs, decision trees, clustering - Proficient in Python (NumPy, Pandas, Scikit-learn, Statsmodels) - Experience with ML libraries like TensorFlow, PyTorch - Strong analytical and critical thinking skills - Ability to convert vague requirements into well-defined problems - Exposure to graph theory, time-series analysis, or numerical methods would be a plus If you are looking for a challenging role where you can utilize your mathematical, statistical, and machine learning skills to solve real-world problems, then this position is perfect for you. Join our team and be part of cutting-edge projects that make a difference. (Note: The additional details of the company are not provided in the job description.),