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WDTS is looking seek enthusiastic individuals to join our Data Science team, focusing on multiple key areas in Gaming Industry like Player playing pattern, Fraud detection and bringing efficiency with prediction modelling.


As a Data Scientist , you will play a crucial role in enhancing our systems through the application of advanced mathematics, machine learning, statistics, and operations research techniques.


Key Responsibilities:

1. Responsible for solving non-trivial, high-impact problems impacting consumer experience using machine/deep learning, data mining, statistical analysis, and related techniques

2: Showcase excellent problem-solving skills, and ability to deconstruct and formulate solutions from first-principles

3: Assist in collecting, cleaning, and preprocessing data for analysis and conduct exploratory data analysis to uncover insights and trends.

4: Develop and deploy machine learning models on large-scale datasets.

5: Collaborate with cross-functional teams to understand business requirements and translate them into actionable data science solutions.

6: Stay updated on the latest trends and advancements in data science and machine learning techniques.


Minimum qualifications:

  • Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, a related quantitative field, or equivalent practical experience.
  • 5 years of experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems) and coding (e.g., Python, R, SQL).


Preferred qualifications:

  • Master's degree in Engineering, Business, or quantitative field.
  • Experience in identifying opportunities for business/product improvement and defining/measuring the success of those initiatives.
  • Experience working with Engineers and Product Managers providing product centric insights.
  • Experience in developing new models, methods, analysis and approaches. Experience in one or more of the following methods: time series forecasting, statistical and causal inference, classification methods.


  • If you are passionate about leveraging data to drive business insights and eager to learn and thrive in a dynamic environment, we encourage you to apply for this exciting opportunity.