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 :

1. Mathematics & Statistics :

- Strong understanding of Linear Algebra, Multivariable Calculus, Probability & Statistics

- Hands-on experience with statistical inference, hypothesis testing, and confidence intervals

2. 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.)

3. Technical Skills :

- Proficient in Python (NumPy, Pandas, Scikit-learn, Statsmodels)

- Experience with ML libraries like TensorFlow, PyTorch

- Exposure to Jupyter, Matplotlib/Seaborn, and Git

4. 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 :

1. Exposure to graph theory, time-series analysis, or numerical methods

2. Experience with real-time data pipelines, GraphQL, SQL, or NoSQL

3. Experience in scientific computing or simulation modeling