About the role
Hybrid Opportunity in Philadelphia, PA
At BlueConic, we are revolutionizing the way businesses harness customer data, transforming it into actionable intelligence in real time. As the innovators behind the industry’s first Customer Growth Engine (CGE), we empower brands to transcend traditional Customer Data Platforms (CDPs), adopting a more intelligent, rapid growth model that leverages AI, prioritizes privacy, and delivers significant value throughout the customer journey.
Join us in redefining customer engagement, where cutting-edge machine learning, causal decision-making, and real-time experimentation converge to facilitate optimized actions at scale.
About the Role:
We are seeking a skilled Data Scientist to become a vital member of our Product & Technology team. In this position, you will operate at the nexus of product development and customer-centric data science, contributing to the creation and application of advanced decision-making capabilities that drive real-time personalization, experimentation, and optimization for growth.
Your collaboration with product managers, engineers, and customer teams will be essential for designing, implementing, and operationalizing predictive and decision-making models, integral to BlueConic’s product roadmap and in direct collaboration with customers addressing significant growth challenges.
This hands-on role is ideal for someone who thrives on transforming theoretical concepts into practical applications, moving from models to measurable real-world outcomes.
Key Responsibilities:
- Design, develop, and deploy predictive and decision-making models that enhance BlueConic’s product functionalities, including next-best-action and real-time personalization.
- Utilize and advance causal inference techniques and reinforcement learning (notably contextual multi-armed bandits) to refine customer interactions and assess incremental impact.
- Collaborate with product and engineering teams to incorporate models into scalable, real-time systems.
- Engage directly with customers on advanced use cases, creating and validating models tailored to their unique business goals and data environments.
- Convert business objectives into modeling strategies, clearly communicating results to both technical and non-technical stakeholders.
- Contribute to experimentation frameworks, uplift modeling, and closed-loop optimization methodologies.
- Help establish best practices for data science within the product organization, including standards for model evaluation, testing, and deployment.
