Data Scientist II

Are you looking for a Data Scientist role whereby you will unlock insights from large-scale datasets and build
models to drive success for one of our core business verticals?
About the role: We are seeking a Data Scientist II to join our Acquisition and Retention Analytic team. In this
role, you will apply advanced data science techniques to drive strategic decision-making, optimize marketing
effectiveness, and elevate customer lifetime value in a highly competitive and evolving insurance landscape. 
As part of the Acquisition and Retention Analytics team, you’ll work cross-functionally with marketing, product,
and vertical teams to unlock insights from large-scale datasets and build models that improve campaign
performance, personalize customer engagement, and proactively reduce attrition.

About the team, The Acquisition and Retention Analytics team works cross-functionally with marketing,
product, and vertical teams to unlock insights from large-scale datasets and build models that improve
campaign performance, personalize customer engagement, and proactively reduce attrition. This team’s
products help insurance carriers and financial institutions identify, attract, and retain high-value customers.
This solution leverages advanced analytics, extensive data assets, and proprietary linking technology to
optimize customer acquisition and retention strategies.
Requirements:
 Current and extensive model development in Python or PySpark.
 Possess current work experience in statistical modeling and analysis.
 Possess domain expertise in Data Science and/or Statistical Analysis, with the capability to develop
advanced models and collaborate with cross-functional teams to deploy them into production.
 Ability to design, scope, and implement new statistical methodologies with guidance and approval from
leadership.
 Ability to work across coding languages used in Data Science (e.g. Python, SQL, and R)
 Have current knowledge of machine learning models, with a preference for experience in Natural
Language Processing (NLP) or Large Language Models (LLMs), is a plus.

Responsibilities
 Designing and executing data-driven solutions to quantify the performance of acquisition and retention
efforts. Identify opportunities to improve conversion, lower churn, and optimize lifetime value through
segmentation, predictive modeling, and causal inference.
 Developing, deploying, and maintaining models (e.g., propensity, churn, response, uplift) using
techniques such as logistic regression, random forest, XGBoost, and clustering methods.
 Working with structured and unstructured data from various sources (internal and external), ensuring
quality, consistency, and readiness for modeling and analytics.
 Partnering closely with marketing, product, data engineering, and vertical business stakeholders to
define requirements, align on business goals, and translate insights into actionable outcomes.
 Designing tests and interpreting results to guide marketing strategies, product changes, and retention
interventions.
 Communicating complex analyses and insights to technical and non-technical audiences. Build
dashboards and presentations that drive decision-making and transparency.