Data Scientist

Job Description

- Around 10+ years of advanced 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
- Proven experience working with large-scale structured and unstructured datasets across flat files, relational databases, cloud platforms, and distributed systems
- Strong exposure to GCP and Microsoft Azure and cloud-based analytics/data science environments
- Experience with
Spark, Databricks, and large-scale data processing frameworks
- Experience with analytics and data science tools such as
Dataiku and RapidMiner
- Solid understanding of descriptive statistics, hypothesis testing, EDA, and feature analysis
- Experience in telecom or similarly complex, multi-domain environments preferred
- Strong knowledge and hands-on experience with supervised and unsupervised machine learning methods, including:
- o
Linear and logistic regression
- o Decision trees and tree-based methods
- o Random forest, gradient boosting, and other ensemble techniques
- o Support Vector Machines
- o Clustering methods such as k-means, hierarchical clustering, and DBSCAN
- o Dimensionality reduction techniques such as PCA
- Experience building predictive and classification models for business use cases such as:
- o Customer churn prediction
- o Customer segmentation
- o Revenue forecasting
- o Campaign response and propensity modeling
- o Anomaly and fraud detection
- o Service performance and network issue prediction
- o Customer experience and support interaction analytics
- Experience with time series analysis and forecasting for operational and business trend analysis
- Experience with feature engineering, model validation, hyperparameter tuning, and model performance evaluation
- Strong understanding of model evaluation metrics for regression, classification, and clustering use cases
- Ability to identify the appropriate modeling approach based on business problem, data quality, and operational constraints
- Experience supporting enterprise data environments spanning multiple business functions
- Knowledge of telecom KPIs, subscriber behavior, billing data, network performance data, and customer interaction datasets
- Familiarity with MLOps concepts, model monitoring, and model lifecycle management
- Experience with dashboarding and data visualization tools to present analytical findings effectively
- Familiarity with A/B testing, causal inference, and experimentation frameworks is a plus
- Experience with NLP/text analytics for customer care notes, tickets, surveys, or interaction data is a plus
- Exposure to recommendation systems, optimization methods, or graph/network analytics is a plus
- Strong people skills, team orientation, and professional attitude
- Excellent written and verbal communication skills, with the ability to explain complex technical concepts to business stakeholders

Job Responsibilities

• Apply advanced data science and machine learning techniques to large telecom datasets to identify patterns, trends, and opportunities that improve mission and business decisions
• Partner with stakeholders across marketing, network, IT, billing, customer care, and other business units to understand data challenges and translate them into analytical and modeling solutions
• Develop, validate, and deploy statistical and machine learning models to support cross-functional operational and strategic initiatives
• Analyze enterprise data from multiple source systems and domains to uncover actionable insights, business drivers, operational risks, and performance opportunities
• Build predictive, segmentation, forecasting, and anomaly detection models relevant to enterprise and telecom use cases
• Perform data mining, exploratory data analysis, feature selection, and model diagnostics on large and complex datasets
• Work with structured, semi-structured, and distributed data environments using modern cloud and big data platforms
• Collaborate with data engineers, architects, analysts, and business partners to productionize models and support scalable analytical solutions
• Communicate findings, modeling approaches, assumptions, and recommendations clearly to both technical and non-technical audiences
• Contribute to best practices in data science, model governance, documentation, reproducibility, and analytical standards within the IT organization