Selective is looking to develop and maintain analytical solutions using Data Mining, Machine Learning, Predictive Modeling, Generative AI, and other emerging technologies to address current and future business needs. This involves leading model development, monitoring outcomes, and reporting actionable insights to improve business processes and decision-making.
Requirements
- Professional statistics/quantitative skills.
- Hands-on experience of solving business problem with various multi variate statistical methods including generalized linear model, survival model, time series, mixed effects model etc.
- Familiar with the whole life cycle of predictive modeling, including data provision, model development, validation and testing, model deployment.
- Proficient with manipulating data with enterprise-scale database systems (SQL, SAS).
- Proficient with at least one scripting language such as R, Python, or Java.
- Experience with BI tools such as Tableau, Spotfile etc. is preferred.
- Hands on experience with Big Data / Hadoop / Spark is preferred.
Responsibilities
- Lead the supervised and unsupervised model development efforts in partnership with business and Information Technology (IT) stakeholders from diverse functional areas.
- Proactively monitor outcome post installation and report actionable insights.
- Manipulate high-volume, high-dimensionality data from varying sources to highlight patterns, anomalies, relationships, and trends.
- Perform descriptive and diagnostics analysis to provide actionable insights for a range of business customers via effective visualization.
- Perform prescriptive analysis seeking for statistical inference providing information that will drive strategic decision making related to marketing, claims and underwriting.
- Develop predictive models and machine learning solutions to investigate problems, detect patterns and recommend solutions.
- Work with Information Technology to integrate the model into production environment.
Other
- Understand the competitive marketplace, business issues, and data challenges to deliver actionable insights, recommendations, and business processes.
- Perform model maintenance and post installation monitoring, including performance tracking, model refit/refresh, documentation, and version control.
- Research and prototype advanced data science techniques like Text Mining, Natural Language Processing (NLP), Image Recognition and Robotic Process Automation (RPA).
- Research and recommend new data, tools, or modelling techniques to enrich analytics solutions in alignment to policy lifecycle.
- Participate in the broader community to stay current with industry trends including cutting-edge methodology, software, platform and recommend best practices.
