Data Scientist

Hybrid / Philadelphia, PA At BlueConic, we’re reinventing how businesses grow by transforming customer data into real-time intelligence and action. As the creators of the industry's first Customer Growth Engine (CGE), we help brands move beyond traditional CDPs to a smarter, faster model for growth—powered by AI, built for privacy, and designed to create meaningful value across the customer lifecycle. Join us in shaping the future of customer engagement—where advanced machine learning, causal decisioning, and real-time experimentation come together to power next-best actions at scale. About the role: We’re looking for an experienced Data Scientist to join our Product Technology team. In this role, you’ll work at the intersection of core product development and customer-facing data science, helping build and apply advanced decisioning capabilities that power real-time personalization, experimentation, and growth optimization. You’ll collaborate closely with product managers, engineers, and customer teams to design, implement, and operationalize predictive and decisioning models—both as part of BlueConic’s product roadmap and in direct partnership with customers solving high-impact growth challenges. This is a hands-on role for someone who enjoys moving from theory to production, and from models to real-world outcomes. What you’ll do: Design, build, and productionize predictive and decisioning models that power BlueConic’s product capabilities, including next-best-action and real-time personalization Apply and advance techniques in causal inference, reinforcement learning (specifically: contextual multi-armed bandits) to optimize customer interactions and measure incremental impact Partner with product and engineering teams to embed models into scalable, real-time systems Work directly with customers on advanced use cases, developing and validating models tailored to their business objectives and data environments Translate business goals into modeling strategies, and communicate results clearly to both technical and non-technical stakeholders Contribute to experimentation frameworks, uplift modeling, and closed-loop optimization approaches Help shape best practices for data science within the product organization, including model evaluation, testing, and deployment standards We are looking for: Required: 5+ years of professional experience as a Data Scientist, Applied Scientist, or similar role Strong hands-on experience with causal inference techniques (e.g. uplift modeling, counterfactual analysis, A/B and quasi-experimental methods) Practical experience with reinforcement learning and contextual multi-armed bandits in real-world systems Solid foundation in machine learning, including experience with libraries such as scikit-learn, XGBoost, or equivalent Advanced Python skills, including software engineering best practices (unit testing, modular code, packaging, version control) Experience taking models from experimentation to production environments Preferred: Experience with turning models into Open Neural Network Exchange (ONNX) format Familiarity with cloud data platforms or modern data stacks (e.g. Snowflake, Databricks, feature stores) Experience working on B2C, personalization, marketing, or customer analytics use cases Experience with recommendation algorithms Comfort collaborating directly with customers or external stakeholders on advanced analytical projects You will be a great fit: Enjoy working on problems where measurement, causality, and decision-making really matter Are equally comfortable thinking about algorithms, product impact, and customer outcomes Care about building robust, maintainable data science systems—not just notebooks Like collaborating across disciplines and explaining complex ideas clearly Are curious, pragmatic, and motivated by seeing your work drive real business results Reasons to join us: Be part of a dynamic, professional, and highly collaborative Prod ... (truncated, view full listing at source)