Patriot Mobile is seeking to optimize telecom cost, enhance customer experience, and support strategic business decisions by generating actionable insights from various data systems
Requirements
- Strong knowledge of SQL and relational databases.
- Ability to use Restful APIs to extract JSON data sets for processing into relational databases.
- Expertise in machine learning techniques, predictive modeling, clustering, and anomaly detection.
- Expertise in data visualization skills using tools like Tableau, Power BI, Metabase, etc...
- Proficient in data analysis development tools and languages such as Python and R
- Knowledge of statistical methods, hypothesis testing, A/B testing, and experimental design.
- Strong problem-solving skills with the ability to debug and troubleshoot issues effectively.
Responsibilities
- Analyze large-scale telecom and call center datasets, including CDRs (Call Detail Records), call logs, agent performance data, and staffing data.
- Develop predictive and prescriptive models for customer churn, telecom cost optimization, usage forecasting, staffing optimization, and fraud detection.
- Implement machine learning algorithms, statistical models, and data mining techniques to extract insights and identify trends.
- Design and maintain scalable data pipelines for ingestion and transformation.
- Work with structured and unstructured data using SQL, Python, and modern ETL frameworks.
- Collaborate with cross-functional teams to design experiments, define KPIs, and provide actionable recommendations.
- Create dashboards, visualizations, and reports to communicate insights effectively to both technical and non-technical stakeholders.
Other
- Bachelor's degree in computer science, statistics, mathematics, or a related field from a regionally accredited college.
- Minimum of 3 years of experience as a Data Scientist or Data Analytics Engineer, preferably in telecom, networking, or related technology sectors.
- Ability to communicate complex technical insights to non-technical stakeholders.
- Collaborative mindset and experience working in cross-functional teams.
- Attention to detail and commitment to data quality.
