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Tier-1 brand, metro location, popular Data Scientist title, and broad ML skill requirements drive high competition.
Core ML/AI and production pipeline skills transfer across industries, but telecom domain knowledge raises specificity moderately.
Explicit 7-10 years requirement plus mandatory ML, Python, SQL, cloud and production ML pipeline skills increases strictness.
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Extract insights from large, high-dimensional data to solve complex business problems using advanced statistics, predictive modeling, and pattern recognition.
Design and develop systems to consolidate and analyze unstructured big data sources for actionable insights supporting client services and product enhancement.
May lead projects or teams, contributing key expertise in AI, deep learning, algorithms, and system performance across company-wide data science services.
7-10 years of commercial experience in machine learning pipeline development, testing, deployment, and monitoring.
Strong Python programming skills with clean, maintainable code practices; experience with SQL for data exploration and integration.
Master's degree preferred (or equivalent combination of coursework and professional experience).
Familiarity with cloud platforms such as Google VertexAI, BigQuery, CloudSQL, or Compute Engine.
Experienced in advanced ML techniques including classification, clustering, anomaly detection, and time series forecasting using traditional or deep learning approaches.
Capable of mentoring junior/mid-level data scientists and engineers and comfortable working within Agile development processes.
Communicates complex analytical concepts effectively to both technical and non-technical stakeholders.