The Data Scientist plays a crucial role in supporting the CLM team's data-driven initiatives. This position focuses on applying data science techniques to extract valuable insights from customer data, contributing to the team's efforts in enhancing customer experiences and supporting StarHub's business objectives. The Data Scientist will work closely with senior team members to develop and implement machine learning models, conduct statistical analyses, and translate complex data into actionable recommendations. This role is essential in helping the CLM team leverage advanced analytics to optimize customer lifecycle strategies and drive data-informed decision-making across the organization.
Key Accountabilities
Develop and implement machine learning models to predict customer behavior, churn, and lifecycle events. Conduct exploratory data analysis to identify patterns and trends in customer data.
Perform statistical tests and analyses to validate hypotheses and support data-driven decision-making. Interpret results and communicate findings to team members and stakeholders.
Create clear and impactful visualizations to present complex data insights to both technical and non-technical audiences. Contribute to the development and maintenance of dashboards.
Assist in building and maintaining machine learning pipelines for data preprocessing, model training, and deployment. Collaborate with the data engineering team to ensure efficient data flow.
Apply clustering techniques and other segmentation methods to identify distinct customer groups and their characteristics. Support the development of targeted marketing strategies based on segmentation insights.
Develop predictive models to forecast customer behavior, including purchase propensity, churn risk, and lifetime value. Continuously refine and improve model accuracy.
Work closely with cross-functional teams, including marketing, sales, and product development, to align data science projects with business objectives. Participate in brainstorming sessions and contribute ideas for new analytics initiatives.
Stay updated on the latest advancements in data science techniques, tools, and best practices. Attend relevant workshops, webinars, and conferences to enhance skills and knowledge.
Qualifications Education Level
Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
Master's degree in a relevant field is preferred.
Required Experience and Knowledge
2-3 years of experience in data science, machine learning, or a related field.
Strong foundation in statistical analysis and machine learning algorithms.
Proficiency in Python or R for data analysis and modeling.
Experience with SQL for data manipulation and querying.
Familiarity with data visualization tools such as Tableau, PowerBI, or Matplotlib.
Understanding of customer lifecycle management concepts and metrics.
Job-Specific Technical Skills
Proficiency in Python or R for data analysis and machine learning.
Experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch.
Knowledge of SQL for data extraction and manipulation.
Familiarity with big data technologies like Hadoop or Spark.
Experience with version control systems (e.g., Git).
Understanding of data visualization principles and tools.
Behavioural Skills
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