Amaze Systems /
Role: Data Scientist
Location: Atlanta, GA/ Frisco, TX/ Overland Park, KS/ Seattle, WA (Onsite from day 1 and all 5 days a week)
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 Google Cloud Platform 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
Education:
Bachelor's or Master's degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.
Regards,
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