Data Scientist

Interview Process: 1st Round Face-to-Face Interview

Key Responsibilities:

- Design, develop, and deploy advanced analytics solutions to address complex business challenges and improve operational efficiency.

- Build predictive, prescriptive, and machine learning models that support business decision-making and process automation.

- Analyze large, structured, and unstructured datasets to identify trends, patterns, and actionable insights.

- Collect, cleanse, transform, and validate data from multiple enterprise sources to ensure high-quality analytical outputs.

- Design and implement scalable analytical frameworks and machine learning solutions for production environments.

- Develop interactive dashboards, reports, and visualizations using Power BI, Tableau, or similar tools.

- Collaborate with business stakeholders, product teams, and technology teams to understand requirements and translate them into scalable analytical solutions.

- Optimize machine learning models through feature engineering, model tuning, validation, deployment, and performance monitoring.

- Support cloud-based analytics initiatives and contribute to the development of scalable AI and data science solutions.

- Promote best practices in data science, model governance, documentation, and continuous improvement.

- Stay current with emerging technologies in Artificial Intelligence, Machine Learning, and Advanced Analytics to drive innovation across the organization.

Qualifications & Technical Skills:

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.

- Minimum 7 years of experience in Data Science, Machine Learning, or Advanced Analytics.

- Strong expertise in Advanced Analytics, Statistical Analysis, Machine Learning, Predictive Analytics, and Prescriptive Analytics.

- Proficiency in Python and/or R for data analysis and model development.

- Strong SQL skills with experience in data modeling, data wrangling, and feature engineering.

- Hands-on experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost.

- Experience developing interactive dashboards and reports using Power BI or Tableau.

- Knowledge of cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform (GCP).