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Data Scientist – Customer Lifecycle Management & Insights

SpringCube

Full time - Associate/Junior Executive

Telecommunications, Cable & Satellite

Singapore ( Onsite )

Published 4 weeks ago

Salary: SGD5,000 - SGD10,000

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Job Description

The SpringCube team curated the following job opportunity to help you in your job search. Explore the position above to find your next career move.

Data Scientist – Customer Lifecycle Management & Insights

Company Overview
A leading Singaporean company, this organization provides world-class communications, entertainment, and digital services. Through its extensive infrastructure and innovative solutions, it delivers high-quality connectivity, premium content, and cutting-edge technology, including artificial intelligence, data analytics, cybersecurity, and robotics.

Job Description
The Customer Lifecycle Management (CLM) team specializes in optimizing the end-to-end customer journey, leveraging data science and analytics to enhance experiences, boost loyalty, and meet business objectives. The Data Scientist plays a pivotal role in analyzing customer behavior, building predictive models, and delivering actionable insights that drive customer engagement strategies.

Key Accountabilities

  • Develop and implement machine learning models to predict customer behavior, churn, and lifecycle events.
  • Perform exploratory data analysis to uncover trends and validate hypotheses.
  • Create impactful visualizations and maintain dashboards to communicate complex insights.
  • Collaborate with data engineering teams to ensure efficient machine learning pipelines.
  • Conduct customer segmentation to inform targeted marketing strategies.
  • Partner with cross-functional teams to align analytics projects with business goals.
  • Stay updated with advancements in data science tools and practices.

Qualifications

  • Education: Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field; Master’s preferred.
  • Experience: 2–3 years in data science, machine learning, or related domains.
  • Skills:
    • Strong foundation in statistical analysis and machine learning.
    • Proficiency in Python or R and SQL.
    • Familiarity with visualization tools like Tableau, PowerBI, or Matplotlib.
    • Experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch.
    • Knowledge of big data technologies (e.g., Hadoop, Spark) and version control systems.

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