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Job Benefit

  • Laptop
  • Insurance
  • Travel opportunities
  • Incentive bonus
  • Health checkup
  • Training & Development
  • Salary review
  • Annual Leave

Job Description

  • Data Analysis & Modeling: Develop, implement, and optimize advanced statistical models and machine learning algorithms to solve complex business problems.
  • Data Management: Work with large structured and unstructured datasets to clean, process, and transform data for modeling purposes.
  • Insights & Visualization: Generate actionable insights through exploratory data analysis and create visualizations for effective communication to stakeholders.
  • Collaboration: Partner with cross-functional teams, including engineering, product, and business stakeholders, to identify opportunities, define requirements, and deploy data-driven solutions.
  • Research & Development: Stay up-to-date with the latest advancements in data science, machine learning, and AI technologies to ensure we remain at the forefront of innovation.
  • Deployment: Build and deploy scalable machine learning models into production systems and ensure model performance monitoring and optimization.
  • Mentorship: Provide technical guidance and mentorship to junior data scientists and analysts.

Job Requirement

  • Education: Bachelor’s degree or higher in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
  • Experience: Minimum 5+ years of experience in data science, machine learning, or related fields.
  • Technical Skills:
    • Proficiency in programming languages like Python or R.
    • Hands-on experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn) and machine learning platform stacks (e.g., kubeflow, mlflow, FEAST).
    • Expertise in big data technologies such as Spark, Hadoop, or Hive.
    • Strong SQL skills and experience with databases (e.g., Redshift, Snowflake, BigQuery).
    • Experience with cloud platforms (e.g., AWS, Azure, GCP).
  • Domain Knowledge: Experience in applying data science techniques to business problems like customer segmentation, churn prediction, recommendation systems, or fraud detection.
  • Visualization Tools: Proficiency with visualization tools such as Tableau, Power BI, or Plotly.
  • Soft Skills: Strong problem-solving abilities, excellent communication skills, and the ability to explain technical concepts to non-technical stakeholders.

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