IRC292554 Data Scientist (Client: T-Mobile)

IRC Data Scientist (Client: T-Mobile)

YearofExperience:10+ years

Location:Atlanta, GA/ Frisco, TX/ Overland Park, KS/ Seattle, WA(Onsite from day 1 and all 5 days a week)

IRC

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: Linear and logistic regression Decision trees and tree‑based methods Random forest, gradient boosting, and other ensemble techniques Support Vector Machines Clustering methods such as k‑means, hierarchical clustering, and DBSCAN Dimensionality reduction techniques such as PCA Experience building predictive and classification models for business use cases such as: Customer churn prediction Customer segmentation Revenue forecasting Campaign response and propensity modeling Anomaly and fraud detection Service performance and network issue prediction 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, hyper‑parameter 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 Education

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

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