Senior Associate, Analytics Engineer

Location Designation: Hybrid 

 

 

When you join New York Life, you’re joining a company that values career development, collaboration, innovation, and inclusiveness. We want employees to feel proud about being part of a company that is committed to doing the right thing. You’ll have the opportunity to grow your career while developing personally and professionally through various resources and programs. New York Life is a relationship-based company and appreciates how both virtual and in-person interactions support our culture.

 

 

When you join New York Life, you’re joining a company that values career development, collaboration, innovation, and inclusiveness. We want employees to feel proud about being part of a company that is committed to doing the right thing. You’ll have the opportunity to grow your career while developing personally and professionally through various resources and programs. New York Life is a relationship-based company and appreciates how both virtual and in-person interactions support our culture.

The Center for Data Science and Artificial Intelligence (CDSAi) is an innovative corporate Analytics group within New York Life. We are a rapidly growing entrepreneurial department which aims to design, create and offer innovative data-driven solutions for many parts of the enterprise. We are aided by New York Life’s existing business with a large market share in individual life insurance. We have the freedom to explore external data sources and new statistical techniques and are excited about delivering a whole new generation of predictive analytics and artificial intelligence solutions.

 

In fact, we are building one of the first multivariate model-based continuous risk differentiations in the industry. We are also working on models for differentiated advertising allocation by geography, channel and segment. Geographic analytics on agents and customers, application fraud detection, agent success prediction and client prospecting analytics (off-line and on-line) are other exciting examples of enormous incremental value from analytics. Our products are implemented into real-time core business processes and decisions that drive the company (e.g., underwriting, pricing, agent recruiting, prospecting, advertising allocation, new product development).

 

We work with data ranging from demographics, credit and geo data to detailed medical data (medical test results, diagnosis, prescriptions) and social media information. We have a modern computing environment with a solid suite of data science/modeling tools and packages, and a large (but manageable) group of well-trained professionals at various levels to support you. Life insurance is on the verge of huge change. This is a chance to drive the transformation of an industry.

 

The Center for Data Science and Artificial Intelligence (CDSAi) is the 60-person innovative corporate Data Science group within New York Life. We are a rapidly growing entrepreneurial department which designs, creates, and deploys innovative data-driven solutions for many parts of the enterprise. For more opportunities in data science, please visit our website (https://www.newyorklife.com/careers/corporate/data-science)

 

You will join a team of analytics engineers, to provide clean transformed data for data science solutions. You will build data transformation pipelines which adhere to software and data engineering best-practice standards. You’ll be an instrumental member of the Analytics Engineering team, part of the wider MLOps umbrella, led by Padma Vellanki 

 

Responsibilities

1. Partner with data scientists to explore, analyze, and source data from our strategic data sources.
2. Create data pipelines to provide clean transformed data for data science/analytics use cases.
3. Follow best practices to coding formats, naming conventions, and version control.
4. Review code changes and approve pull requests (PRs) in Git
5. Convert requirements into Jira stories and provide milestones.
6. Partner with data scientists regarding data quality, availability, value, etc.
7. Collaborate with data stewards throughout NYL.
8. Build strong relationships with Technology (IT) to work on tooling, data strategy, integrations, and deployments.
9. Effectively communicate information and ideas to a diverse group of people
10. Stay up to date with the latest Analytics Engineering trends/emerging technologies and look for opportunities to improve the stack.

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