Description

You will apply statistical modeling and machine learning techniques to drive data insights and build predictive solutions.

This role is hybrid.

Responsibilities

• Perform statistical analysis including hypothesis testing (T-Test, Z-Test) and regression modeling (Linear, Logistic).
• Develop and implement forecasting models using Exponential Smoothing and ARIMA.
• Build and evaluate classification models using Decision Trees and SVM.
• Utilize ML frameworks to build and deploy predictive models at scale.

Required Skills

• 5+ years of experience in data science or a related analytical field.
• Proficiency in Python and PySpark for large-scale data processing.
• Experience with statistical software such as SAS and SPSS.
• Hands-on experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn, CNTK, Keras, or MXNet.
• Familiarity with probabilistic graph models and statistical computing.
• Knowledge of distance metrics (Hamming, Euclidean, Manhattan Distance).
• Ability to apply core statistical concepts including probability and hypothesis testing.

Key Skills

Education

Any Gradute