Data Science Manager

Job description

Top Reasons to work with TDCX

• Attractive remuneration, great perks, and performance incentives
• Comprehensive medical, insurance, or social security coverage
• World-class workspaces
• Engaging activities and recognition programs
• Strong learning and development plans for your career growth
• Positive culture for you to #BeMore at work
• Easy to locate area with direct access to public transport
• Flexible working arrangements
• Be coached and mentored by experts in your field
• Join a global company, winner of hundreds of industry awards

What is your mission

• Work with stakeholders throughout the organization to identify opportunities for leveraging data to drive business solutions.
• Mine and analyze data from databases to drive optimization and improvement of product development, marketing techniques and business strategies.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Develop custom data models and algorithms to apply to data sets.
• Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
• Develop processes and tools to monitor and analyze model performance and data accuracy.
• Any other duties and responsibilities that may be assigned to you by the management from time to time, within your category of employment in the organization and for the effective implementation, maintenance and continual improvement of the Quality Management System of TDCX.

Who are we looking for

• Candidate must possess at least a Advanced/Higher/Graduate Diploma, Bachelor's Degree, Post Graduate Diploma, Professional Degree in Statistics, Mathematics, Computer Science or another quantitative field.
• Preferable with 2 years of working experience in the related field is required for this position.
• Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
• Experience working with and creating data architectures.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Excellent verbal and written communication skills in English and the language of supporting market