• Responsible for leading the development, validation and delivery of algorithms, statistical models and business analysis, as well as management of all analytics jobs handled by PACS Analytics CoE.
• Conduct a code review and pool request of all analytics jobs handled by PACS Analytics CoE.
• Develop algorithms and statistical predictive models and determine analytical approaches and modelling techniques to evaluate scenarios and potential future outcome.
• Perform analyses of structured and unstructured data to solve multiple and/or complex business problems utilizing advanced statistical techniques and mathematical calculations and broad knowledge of the organization and/or industry.
• Drive insights into the acquisition, conversion or retention of a customer across marketing, operations, claims by continuously analyzing the end to end customer journeys.
• 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.
• Design the framework to understand the principles which underlie customer and distributor behaviour, with the purpose of aligning distribution objectives with customer needs and driving incremental customer base, customer loyalty, product profitability and sustainable growth
• Collaborate with business partners to understand their problems and goals and develop predictive modelling, statistical analysis, data reports, and performance metrics.
• Train ML models based on predictive algorithms.
Who we are looking for:
Competencies & Personal Traits:
- Subject matter expertise in machine learning tools and technologies including Tensorflow, Spark / Spark MLib, Flink, Mahout or any packaged cognitive solutions
- Subject matter expertise in machine learning concepts and techniques(linear/logistic regression, clustering, classification, principal 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.
- Statistics experience in propensity score matching, coarsened exact matching, multivariate regression, etc.
- 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.)
- Database programming in SQL as well as SAS.
- Familiarity in data visualization libraries or tools such as Matplotlib, plot.ly, C4.js, Dataiku, Tableau, Highcharts, ggplot etc.
- Handling large datasets on distributed architectures like Hadoop/Spark and open-source data mining and machine learning frameworks.
- Creating interface specification documents, attribute mapping documents, functional specifications.
- Version control tools (Git, SVN)
- Translating business problems and use cases into appropriate models.
- Building, optimizing and deploying models to produce actionable insights.
- Presenting results and recommendations to non-technical business partners and stakeholders to drive decision-making and actions.
- Applying recent academic developments in data science to business problems.
- Proven track record of at least 3-5 years in in a similar role.
- 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.