Description

You will design, build, and validate statistical models while migrating legacy analytical processes to Python.

Responsibilities

• Migrate Excel-based analytical, financial, and statistical models to Python, ensuring feature parity and performance improvement.
• Design and validate regression and statistical models, including Linear, Logistic, Elastic Net, and Time-series models.
• Clean, transform, and analyze large datasets, performing feature engineering and handling data quality issues.
• Package models into versioned Python modules or pipelines, writing unit tests and automation scripts.
• Translate business questions into modeling problems and communicate insights and model outcomes to non-technical stakeholders.

Required Skills

• 7–10 years in analytics or data science.
• Demonstrable proficiency in Python (pandas, numpy, statsmodels, scikit-learn).
• Strong command of regression techniques, model diagnostics, and cross-validation.
• Proven experience converting complex Excel models into Python pipelines.
• SQL proficiency for data extraction and joins.
• Solid experience with Exploratory Data Analysis (EDA) and data quality practices.
• Version control using Git and strong documentation habits.
• Experience with visualization tools like matplotlib, seaborn, or plotly.

Preferred Skills

• Workflow orchestration with Airflow or Prefect.
• Exposure to cloud environments (AWS, GCP, Azure).
• Familiarity with CI/CD for analytics.

Key Skills

Education

Any Gradute