DMI, LLCis seeking a Data Scientist to join us. As a Data Scientist on our team, you will be responsible for designing and implementing advanced data models, analytics solutions, and machine learning algorithms that enable us to deliver accurate, actionable insights from large telecom datasets. You will leverage your deep understanding of data science, statistical analysis, and machine learning to solve complex business problems, such as telecom expense optimization, billing reconciliation, and usage forecasting. Your work will directly impact product innovation, improving the decision-making process for both our internal teams and external customers.
In this role, you will collaborate with cross-functional teams, including Database Administrators, Developers, Product Managers, and Customer Success Teams. You will also have the opportunity to shape the data strategy that powers the next generation of features within our Telecom Expense Management platform.
Duties and Responsibilities:
Data Analysis and Modeling:
- Analyze large-scale datasets, primarily telecom billing and usage data, to uncover insights and develop data-driven strategies for improving expense management and customer outcomes.
- Build, deploy, and refine predictive models using machine learning techniques such as regression, classification, clustering, and time series forecasting, to support customer cost optimization and forecasting needs.
- Design advanced statistical models and algorithms to automate error detection and anomaly recognition, improving the accuracy and efficiency of billing reconciliations.
Machine Learning and Predictive Analytics:
- Develop machine learning models that can forecast telecom usage patterns, identify cost-saving opportunities, and detect billing discrepancies.
- Continuously improve and retrain machine learning models to enhance accuracy, performance, and scalability.
- Create robust systems for identifying patterns in telecom data, such as fraud detection, usage anomalies, and outlier identification to mitigate risks for our clients.
Data Infrastructure and Pipeline Development:
- Work closely with the Database Administrator and Development Teams to design, build, and maintain data pipelines that efficiently process telecom data from multiple sources, ensuring data quality and integrity.
- Collaborate on the architecture of large-scale ETL (Extract, Transform, Load) processes, ensuring the efficient handling of high-volume telecom billing and usage data.
- Use cloud-based tools and services, primarily in AWS, to automate and optimize data workflows, ensuring seamless integration with the platform.
Business Intelligence and Reporting:
- Develop intuitive and insightful dashboards, reports, and visualizations using tools like Tableau, Power BI, or Python-based libraries to translate complex data into actionable insights for both internal stakeholders and external clients.
- Provide insights and recommendations to drive product decisions, such as enhancements to cost analytics and telecom optimization tools, based on data trends and user behavior.
- Collaborate with the Product and Customer Success teams to tailor data-driven solutions to customer needs, improving the overall user experience of the TEM platform.
Collaboration and Thought Leadership:
- Partner with cross-functional teams, including Engineering, Product Management, and Service Delivery, to ensure data solutions align with customer needs and business objectives.
- Provide guidance to junior data scientists or data engineers, fostering a culture of learning and innovation within the team.
- Stay up to date with the latest trends and advancements in data science, machine learning, and the telecom industry to keep the company at the forefront of innovation.
Innovation and Data Strategy:
- Identify and explore new opportunities for leveraging advanced data science techniques to expand and improve the platform’s features and capabilities.
- Influence the company’s overall data strategy by recommending new ways to enhance data processing, integration, and analytics, contributing to long-term product innovation.
- Lead initiatives to explore and evaluate emerging technologies in data science, cloud computing, and AI that could bring a competitive advantage to the platform.
