Data Scientist – AI, Analytics & Machine Learning

We are seeking a highly motivated Data Scientist to join our analytics and AI team focused on driving data-driven decision-making across large-scale systems and telecommunications-related use cases. You will work on advanced data analytics, machine learning modeling, and statistical analysis to extract actionable insights, build predictive systems, and support strategic business and product decisions.

• Analyze large, heterogeneous datasets to identify patterns, trends, and insights that support business and product strategy.
• Design, develop, and deploy machine learning and statistical models to solve real-world problems (predictive analytics, classification, regression, clustering).
• Work closely with product managers, data engineers, and cross-functional teams to translate business requirements into scalable data science solutions.
• Perform exploratory data analysis, feature engineering, and model evaluation.
• Build data pipelines and integrate models into production systems where relevant.
• Communicate insights and results clearly to technical and non-technical stakeholders through visualizations and reports.
• Stay current with emerging techniques in machine learning, artificial intelligence, and data science best practices.

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline.
• Strong programming skills in Python and experience with data science libraries (e.g., pandas, Num Py, scikit-learn).
• Experience with machine learning modeling techniques, including supervised and unsupervised methods.
• Solid statistical analysis skills and understanding of modeling assumptions and performance evaluation.
• Experience working with large datasets and data engineering tools (e.g., SQL, Spark).
• Ability to produce meaningful visualizations and presentations of analytical results.
• Strong problem-solving skills and ability to work collaboratively in cross-functional teams.

• Experience with big data technologies and distributed computing frameworks (e.g., Spark, Hadoop).
• Familiarity with cloud-based data platforms (AWS, Azure, GCP).
• Knowledge of deep learning frameworks (e.g., Tensor Flow, PyTorch) or experience with natural language processing.
• Exposure to AI/ML model deployment workflows
  , MLOps tools, and CI/CD pipelines.
• Experience with real-time streaming or event-driven analytics (e.g., Kafka).