Data Scientist

Key Responsibilities

1. Data Modeling & Analysis

• Design and develop statistical and machine learning models to solve complex business problems in telecom, including customer behavior analysis, network optimization, and fraud detection.
• Perform exploratory data analysis, data wrangling, and feature engineering to prepare high-quality datasets for modeling.

2. Model Development & Deployment

• Build, train, evaluate, and deploy predictive models using Python and industry-standard ML frameworks.
• Work with engineering teams to deploy models into production environments, ensuring scalability and performance.

3. Business Collaboration

• Partner with business stakeholders, product managers, and engineers to understand business needs and translate them into data science solutions.
• Present insights and model results in a clear and concise manner to both technical and non-technical audiences.

4. Data Infrastructure & Tooling

• Utilize cloud platforms (AWS, GCP, Azure) to build scalable data pipelines and ML workflows.
• Leverage tools like TensorFlow, PyTorch, Scikit-learn, and Spark to handle large-scale data processing and modeling.

5. Continuous Improvement & Research

• Stay current with the latest developments in AI/ML and apply new techniques to telecom-specific challenges.
• Contribute to the development of best practices and guidelines for data science within the team.

Skills and Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field.

Technical Expertise

• Strong foundation in statistics, machine learning, and data mining techniques.
• Proficiency in Python and relevant libraries (Pandas, NumPy, Scikit-learn, etc.).
• Experience with ML frameworks (TensorFlow, PyTorch) and data processing tools (Spark, SQL).
• Hands-on experience with cloud services and data pipelines (AWS, GCP, or Azure).
• Telecom experience (e.g., working with network data, customer analytics) is a plus.

Analytical & Problem-Solving Skills

• Ability to analyze large datasets, extract insights, and apply them to solve business problems.
• Strong critical thinking skills with attention to detail and accuracy.

Communication & Collaboration

• Effective communicator with the ability to explain complex concepts clearly.
• Comfortable working in cross-functional teams and contributing to a collaborative environment.

Preferred Qualifications

• Experience in telecom use cases such as customer churn prediction, network performance analysis, and anomaly detection.
• Familiarity with containerization and deployment tools (Docker, Kubernetes).
• Knowledge of CI/CD practices and MLOps frameworks for model lifecycle management.

Exposure to NLP, time-series forecasting, or deep learning techniques