Analyst II Data Science – Third‑party data evaluation

Are you passionate about finding improvements in models using third party data with applications across pricing, underwriting, sales, service, distribution & marketing? Then this is your next job!

We are seeking a data scientist with deep expertise in third‑party data evaluation to support predictive models across the enterprise. This role sits at the intersection of advanced analytics, external data ecosystems, and business decision‑making.

The successful candidate will focus on identifying, testing, and quantifying the incremental value of third‑party data assets across a diverse set of models, including pricing & underwriting, sales & service, and distribution & marketing. You will partner with modeling teams and business stakeholders to ensure external data is used thoughtfully, responsibly, and in ways that measurably improve model performance and business outcomes.

This role requires strong technical depth, sound statistical judgment, and the ability to translate complex analytical findings into clear, actionable insights for non‑technical audiences.


Responsibilities

Lead the research and evaluation of third‑party data sources across multiple modeling domains, including pricing & underwriting, sales & service, and distribution & marketing

Quantify the incremental lift, tradeoffs, and risks associated with third‑party data usage in predictive models, using rigorous statistical diagnostics and validation techniques

Partner with data science and business teams to design and test modeling strategies that responsibly leverage external data while aligning with business objectives

Mine large, complex datasets—both internal and external—using advanced analytical methods to enhance existing models and inform new modeling approaches

Translate quantitative analyses into clear visualizations and narratives that enable stakeholders to understand model impacts and make informed trade‑off decisions

Serve as a technical consultant on projects involving third‑party data, guiding model design decisions and best practices across teams

Engage with the broader data science community and cross‑functional partners to stay current on emerging third‑party data trends, tools, and methodologies




About the US Data Science Function

USDS employs over 350 full-time professionals who build advanced data science products and AI solutions for our US Retail Market (USRM) business. By thoughtfully applying data science across USRM functions, we ensure we consistently deliver on our promises while driving sustainable growth across our portfolio.

We accomplish this through a focus on model development, deployment, and decision science. In model development, we design sophisticated models that combine deep data science expertise with close collaboration across our go-to-market verticals. This integration ensures our models not only incorporate cutting-edge science but, more importantly, generate measurable business value aligned with each vertical’s strategic goals.

Model deployment is equally critical—no matter how advanced a model is, it is ineffective if it cannot be operationalized. Our robust deployment capabilities translate insights into actionable value, prioritizing speed, scalability, and reliability.

Beyond driving specific business actions such as pricing policies or underwriting decisions, we also leverage decision science to inform critical strategic decisions. This ensures that data science supports the best possible outcomes not only in daily operations but also in long-term strategy.




Qualifications

Broad and deep knowledge of predictive analytics, statistical modeling techniques, and model diagnostics, with demonstrated experience assessing external data value

Strong programming skills in Python and familiarity with GLMs and GBMs for predictive modeling

Strong ability to communicate complex analytical concepts clearly and concisely to both technical and non‑technical audiences

Proven experience influencing decisions by using data‑driven insights to articulate tradeoffs, expected outcomes, and business impact

Demonstrated ability to collaborate across functions and build strong partnerships within and outside the data science organization

Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science, or other scientific field of study) and no professional experience, a Master’s degree (scientific field of study) and 2 to 3 years of relevant experience, or may be acquired through a Bachelor’s degree (scientific field of study) and 4+ years of relevant experience

Demonstrate business professional proficiency in English with excellent written and verbal communication skills, as all work and communication are conducted in English.