Senior Data Scientist
Do you enjoy problem solving, quantitative reasoning, and technology? Are you interested in working in a collaborative, friendly environment? If so, we invite you to consider working as a data scientist at Aioi Nissay Dowa Insurance! Our team is responsible for a broad range of data, research, and analytical projects.
We build products and knowledge to help business leaders make better, data-driven decisions. The Advanced Technology Group requires analytic agility, the ability to quickly learn new modeling/ machine learning techniques, programming languages, and see how these ideas can integrate to optimize the business.
This role reports to the Director of Research and Development and is responsible for leading the use of data to build business and customer-facing products for Aioi Nissay Dowa Insurance Services USA Corporation. This includes the coding and development of tools that use machine learning/ predictive modeling to identify driving behavior patterns and other behaviors, searching for and integrating new data (both internal and external) that improves our modeling and machine learning results (and ultimately our decisions), and discovery of solutions to business problems that can be solved using machine learning/predictive modeling.
- Works with research team to identify data requirements, available data sources (internal and external) and expected outcomes for vehicle research and experiments.
- Recommends and supports data collection, integration, and retention requirements by assessing the effectiveness and accuracy of data sources and data collecting techniques.
- Explores and finds meaning in high volumes of data and extracts actionable insights. Executes data querying, data cleansing, and experiment design.
- Applies advanced statistical and predictive modeling techniques and machine learning to validate findings, and to build, maintain and improve models, using an iterative approach.
- Draws from prior experience and technical expertise to identify improvements and inform testing plans; breaks overall objectives down into underlying problems that can be prioritized and solved. Executes and monitors project plans for timely project completion.
- Uses best practices to develop high-performing models that comply with regulatory and privacy requirements that also satisfy business objectives and customer needs.
- Produces documentation and reports, both technical and non-technical. Presents actionable analysis, ideas, progress reports and results to internal managers and executives.
- Maintains and enhances the quantitative pipeline required for quickly moving proof-of-concepts into production. Owns end-to-end data science projects from the exploration phase to implementation in production to post-production monitoring. Coordinates internally to develop rigorous pre- and post-deployment frameworks and integration.
- Leads advanced analysis for products, including utilizing. Research deep learning methods for calibration and fusion of vehicle data, including sensor and camera data.
- Continuous improvement of the predictive power of models. Constantly searches for new features for products, using both traditional and non-traditional data sources.
- Provides ongoing tracking and monitoring of performance of the statistical models, recommends ongoing improvements to methods and algorithms that lead to findings, including new information.
- Reviews and evaluates appropriateness of techniques, given current modeling practices, to senior leadership. Communicates findings to team and leadership to ensure models are well understood and incorporated into business processes. Works with leaders to ensure the project will meet needs.
- Uses and learns a wide variety of tools and languages to achieve results.
- Lead and mentor other data scientists and engineers as needed on best practices for the design and implementation of cutting-edge solutions. Propel data science research as a thought leader to shape next-gen solutions.
- Prefer graduate-level degree (M.S. or higher) with concentration in a quantitative discipline such as statistics, mathematics, economics, operations research, computer science or aligned discipline; or equivalent work experience.
- Five (or more) years of progressively complex related experience; preferably in the insurance, automotive, or financial industries.
- At least 3+ years of experience in developing and productionizing models.
- At least 3+ years of experience in machine learning infrastructure using AWS, GCP or Azure.
- Proficient across all stages of the data science pipeline, from ETL to implementation in production at scale, helps with knowledge transfer.
- Breadth and depth in the areas of feature engineering and selection methodologies, and machine learning methodologies.
- Experience developing, building, and scaling machine learning models in business applications using large amounts of data.
- Comfortable performing exploratory research and working with limited data to create preliminary models.
- Knowledge of statistical modeling, machine learning, mathematical optimization, and/or data mining.
- Experience with big-data tools (preferably Spark) required, and the ability to code and develop prototypes in Python preferred; R or Java acceptable as well.
- Team-oriented with a track record of building strong internal and external relationships.
- Self-starter with strong logical, evidence-based problem solving, and critical thinking skills that support innovative, creative solutions.
- Ability to solve problems, think critically, and communicate clearly.
- Ability to learn new technology quickly.