Data Scientist
18 hours ago
Frisco
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