Lead Engineer Data Science

Telecom Domain needed.

Job Summary:

Build and maintain advanced data science models and analytical tools to generate actionable insights in the telecom sector. Leverage domain knowledge of telecom networks and services to implement scalable data-driven solutions supporting business decisions and product enhancements.

Key Responsibilities:

- Develop statistical and machine learning models for telecom-specific problems such as churn analysis, network performance (NQES, SINR), and customer behavior analysis around Video on Demand and FWA services.

- Prepare, preprocess, and analyze large telecom datasets from wireline and wireless networks, sites, and interfaces.

- Collaborate with software engineers to integrate data science outputs into operational telecom systems.

- Conduct exploratory data analysis and create visualizations to communicate telecom insights effectively.

- Ensure reproducibility, version control, and quality of data science experiments.

- Present findings and recommendations to technical and business stakeholders.

Skills and Requirements:

• Proficiency in Python or R with libraries for data manipulation (Pandas, Num Py), visualization (Matplotlib, Seaborn, Plotly).

• Composer, Data Proc over GCP, Vertex.
  
  AI, Big Query, Teradata

• Understanding H2O is a plus

• Strong foundation in statistics, hypothesis testing, and ML techniques applied to telecom data.

• Familiarity with telecom data sources and metrics including churn, SINR, real-time network data, and service usage patterns.

• Experience with SQL and No
  
  SQL databases managing telecom data.

• Knowledge of cloud-based data platforms and telecom analytics is a plus.

Lead Engineer (Data Science):

Job Summary:

Drive telecom data science projects from conception through deployment while leading a multidisciplinary team. Deliver high-quality data-driven solutions focused on telecom network analytics, customer churn, real-time monitoring, and service optimization.

Key Responsibilities:

- Oversee development, validation, and deployment of data science models solving telecom-specific challenges such as churn, QoS, SINR, and Video on Demand analytics.

- Lead data engineering efforts to build scalable data pipelines from telecom wireline/wireless network sources and real-time data streams.

- Manage team priorities, resources, and timelines ensuring alignment with telecom business objectives.

- Facilitate collaboration across data science, engineering, and telecom domain experts.

- Promote best practices in code quality, testing, and model governance within telecom projects.

- Communicate project progress and technical findings to stakeholders.

Skills and Requirements:

• Proven leadership experience in telecom data science or analytics projects.

• Strong skills in statistical modeling, ML, data engineering with telecom data expertise.

• Proficiency in Python, SQL, and experience with big data platforms (Spark, Hadoop).

• Familiarity with telecom domain knowledge:
  Wireline, Wireless, NQES, churn, real-time data.

• Excellent organizational, communication, and leadership skills.

• Airflow, Data Proc over GCP, Vertex.
  
  AI, Big Query, Teradata

• Understanding H2O is a plus