Data Scientist

16 hours ago

Manila, National Capital Region, Philippines Thakral One Full time ₱1,500,000 - ₱2,500,000 per year

Job Description:

We are seeking a highly experienced and motivated Data Scientist to join our team, with a focus on the banking, payments, or related industries. The ideal candidate will bring over 5 years of hands-on analytics experience and a deep proficiency in statistical modeling, machine learning techniques, and programming tools. This individual will work closely with business and technical teams to leverage data insights and develop advanced predictive models that drive strategic decision-making.

Key Responsibilities:

  • Lead the development and deployment of machine learning models and statistical analyses to address complex business problems within the banking and payments industries.
  • Utilize advanced data programming tools such as SAS, Hadoop, R, SQL, Python, and Hive to analyze large and complex datasets, ensuring data integrity and optimization of analytical processes.
  • Apply a wide range of statistical techniques including Neural Networks, Gradient Boosting, Linear & Logistic Regression, Decision Trees, Random Forests, Markov Chains, Support Vector Machines, Clustering, Principal Component Analysis, and Factor Analysis to derive actionable insights.
  • Develop and implement predictive models, conduct experiments, and validate findings to guide business strategies and operational improvements.
  • Collaborate with cross-functional teams to communicate insights and findings effectively, providing clear data visualizations and actionable recommendations using Excel and PowerPoint.
  • Lead data storyboarding and presentation efforts, ensuring that technical findings are communicated clearly and persuasively to both technical and non-technical stakeholders.

Required Qualifications:

  • 5+ years of hands-on experience in data science, analytics, or a related field, with a focus on banking, payments, or a similar industry.
  • Extensive experience with data analysis and programming tools such as SAS, Hadoop, R, SQL, Python, and Hive.
  • Deep proficiency in statistical modeling and machine learning techniques, including Neural Networks, Gradient Boosting, Linear & Logistic Regression, Decision Trees, Random Forests, Markov Chains, Support Vector Machines, Clustering, Principal Component Analysis, and Factor Analysis.
  • Strong ability to create compelling data visualizations, presentations, and data stories using tools such as Excel and PowerPoint.
  • Excellent problem-solving and analytical thinking skills with the ability to translate complex data into actionable insights.
  • Proven ability to work in a fast-paced environment and manage multiple projects simultaneously.

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