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Data Scientist
  • Python
  • Spark
  • PySpark
  • SQL
  • SAS
  • Machine Learning
  • ggplot
  • Tableau
  • Excel
  • Database
  • Data Mining
  • Data Visualization
  • Modeling
  • Hadoop
  • Scala
  • Business Analysis
Prudential
Singapore
109 days ago

At Prudential, we understand that success comes from the talent and commitment of our people. Together, we have a shared vision in securing the future of our customers and our communities. We strive to build a business that you can shape, an inclusive workplace where everyone’s ideas are valued and a culture where we can thrive together. Our people stay connected and tuned in to what’s happening around us, keeping us ahead of the curve. While focused on the long-term, we look to the future to bring growth, development and benefit to everyone whose lives we touch.

Data Scientist will be part of Data Science workstream within Analytics Centre of Excellence (CoE), responsible for empowering Prudential Singapore’s current decision sciences with advanced statistical modelling and machine learning capabilities, in line with enterprise ambition to be truly data driven.

  • Responsible for leading the development, validation and delivery of algorithms, statistical models and business analysis
  • Develops algorithms and statistical predictive models and determines analytical approaches and modeling techniques to evaluate scenarios and potential future outcomes
  • Lead and coach Data Science team members on latest machine learning algorithms including decision trees, probability networks, association rules, clustering, regression, and neural networks
  • Performs analyses of structured and unstructured data to solve multiple and/or complex business problems utilizing advanced statistical techniques and mathematical analyses and broad knowledge of the organization and/or industry
  • Continuously analyze the end to end customer journey to help drive insights into the acquisition, conversion or retention of a customer across marketing, operations, claims or other areas within Prudential Singapore
  • Provides tactical marketing and analysis support to internal customers including: extensive customer profiling, segmentation, customer lifetime value analysis, response and attrition models, holistic customer business views, in-depth campaign assessment and local market opportunity, planning and analysis
  • Generate new calculated features that improve the predictive of statistical relationships
  • Condense complex analysis and technical concepts into clear and concise takeaways for business leaders
  • Collaborates with business partners to understand their problems and goals, develop predictive modeling, statistical analysis, data reports and performance metrics.
  • Train ML Models based on predictive algorithms
  • Use strong knowledge in algorithms and predictive models to investigate problems, detect patterns and recommend solutions.

Who we are looking for:

  • Subject matter expertise in machine learning tools and technologies including Tensorflow, Spark / Spark MLib, Flink, Mahout or any packaged cognitive solutions
  • Subject matter expertise with application of machine learning concepts and techniques (linear/logistic regression, clustering, classification, principle component analysis, Naive Bayes, association rules, collaborative filtering, recommendation techniques, neural networks).
  • Deep proficiency in at least one language for statistical and scientific computing including Python, R, Scala, or PySpark
  • Strong knowledge of advanced analytics tools and languages to analyze large data sets from multiple data sources.
  • Statistics experience in propensity score matching, coarsened exact matching, multivariate regression, etc
  • Demonstrates proficiency in most areas of mathematical analysis methods, machine learning, statistical analyses, and predictive modeling and in-depth specialization in some areas.
  • In depth knowledge of advanced statistical theories, methodologies, and inference tools (e.g. familiar with hypothesis testing, (generalized) linear models, additive models, mixture models, non-parametric models, etc.)
  • Experience in database programming in SQLas well as SAS.
  • Familiarity in data visualization libraries or tools such as Matplotlib, plot.ly, C4.js, Dataiku, Tableau, Highcharts, ggplot etc
  • Experience in handling large datasets on distributed architectures like Hadoop/Spark and open source data mining and machine learning frameworks
  • Experience in creating interface specification documents, attribute mapping documents, functional specifications
  • Experience in web scraping
  • Experience with Text Matching applications
  • Practical experience with version control tools (Git, SVN)

Qualifications

  • Master’s degree or PhD or equivalent work experience in Mathematics, Statistics, Computer Science, Business Analytics, Economics, Physics, Engineering, or related discipline.
  • Knowledge of life insurance practices preferred

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