Data Scientist

• Apply advanced data science, statistical modeling, and analytics techniques to large and complex datasets.
• Develop and deploy machine learning models to support business and operational decision-making.
• Collaborate with cross-functional teams to translate business problems into data-driven solutions.

Requirements/Must Have:

• Around 10+ years of hands-on experience in data science, statistical modeling, and analytics using Python and R.
• Strong SQL skills, including complex joins, aggregations, window functions, sorting, and query optimization.
• Experience working with large-scale structured and unstructured datasets across multiple platforms.
• Strong exposure to cloud platforms such as Google Cloud Platform and Microsoft Azure.
• Experience with Spark, Databricks, and large-scale data processing frameworks.
• Strong understanding of descriptive statistics, hypothesis testing, exploratory data analysis, and feature analysis.
• Hands-on experience with supervised and unsupervised machine learning techniques.
• Experience in feature engineering, model validation, hyperparameter tuning, and performance evaluation.
• Ability to select appropriate modeling approaches based on business problems and data constraints.
• Strong communication skills with the ability to explain technical concepts to non-technical stakeholders.

Responsibilities:

• Apply machine learning and data science techniques to large datasets to identify patterns, trends, and insights.
• Partner with stakeholders across various business units to understand requirements and deliver analytical solutions.
• Develop, validate, and deploy predictive and statistical models.
• Analyze enterprise data to uncover insights, risks, and performance opportunities.
• Build models for prediction, segmentation, forecasting, and anomaly detection.
• Perform data mining, exploratory data analysis, and feature selection on complex datasets.
• Work with structured and distributed data environments using modern data platforms.
• Collaborate with engineering and analytics teams to deploy scalable solutions.
• Communicate findings and recommendations to technical and non-technical audiences.
• Contribute to best practices in data science, model governance, and documentation.

Should Have:

• Experience in telecom or similar complex, multi-domain environments.
• Knowledge of telecom KPIs, subscriber behavior, billing data, and network performance data.
• Familiarity with MLOps concepts, model monitoring, and lifecycle management.
• Experience with data visualization and dashboarding tools.
• Familiarity with A/B testing, causal inference, and experimentation frameworks.
• Experience with NLP and text analytics.
• Exposure to recommendation systems, optimization methods, or graph analytics.

Skills:

• Proficiency in Python, R, and SQL.
• Experience with machine learning algorithms such as regression, decision trees, random forest, gradient boosting, and support vector machines.
• Knowledge of clustering techniques, dimensionality reduction, and time series analysis.
• Strong analytical and problem-solving skills.
• Excellent verbal and written communication skills.
• Strong teamwork and collaboration skills.

Qualification And Education:

• Bachelor s or Master s degree in Computer Science, Engineering, Mathematics, or a related field.