About Rewards Network
For 41 years, Rewards Network has been helping restaurants grow revenue, increase traffic, and boost customer engagement through innovative financial, marketing services, and premier dining rewards programs. By offering unique card-linked offers, we introduce diners to fantastic restaurant experiences, leveraging advanced technology and data analytics to deliver value to restaurants, diners, and our strategic partners' loyalty programs.
Our Culture
At Rewards Network, you'll be part of a driven and diverse team that excels in collaboration, issue resolution, and taking ownership of both personal growth and the company's success. We take pride in partnering with the world's most powerful loyalty programs to drive full-price paying customers to local restaurants through marketing services and flexible funding options. Our engaging and rewarding environment is designed to help you gain your full potential.
Job Overview
We're looking for a Senior Data Scientist to help lead the technical evolution of a large-scale personalization and assignment platform. You'll design and own the data systems that match members to offers — from batch pipelines processing hundreds of millions to billions of records to the scoring frameworks that decide what each member sees.
This is a senior individual contributor role with technical leadership scope. You won't have direct reports, but you'll help set technical direction, guide junior data scientists and engineers, and own workstreams end-to-end. The platform is actively evolving toward ML-driven personalization and generative AI, and we're looking for someone who wants to help shape that direction.
This is a hybrid position that requires in office presence 3 days a week (Tuesday-Thursday) in Chicago.
What you’ll bring to the table: (Responsibilities)
- Design and own the scoring framework that ranks eligible offers per member — defining features, weighting logic, and validating against business outcomes, then evolving it from deterministic scoring toward ML-driven personalization.
- Lead segmentation and feature pipelines: member group construction, derived attributes, bucketing strategy, and reusable feature sets for eligibility evaluation and targeting.
- Architect and optimize large-scale batch processing workflows handling hundreds of millions to billions of records, including partitioning, bulk ingestion, and performance tuning.
- Define and operate SLAs across the pipeline: batch completion, feed delivery, attribute freshness, and assignment turnaround.
- Provide architectural guidance on a near-real-time assignment API layer and its integration with the broader batch pipeline.
- Define and maintain data contracts with downstream consumers (analytics marts, dashboards, adjacent platforms) and oversee the incremental build-out of analytics data models.
- Translate between business stakeholders (product, marketing, finance) and the engineering team — comfortable holding a business conversation and a technical one in the same meeting.
- Document architecture, data models, pipeline logic, and feature generation processes to reduce key-person dependency and support team continuity.
- Shape the future roadmap for personalization and recommendations, including A/B testing frameworks, behavioral modeling from member activity, and the role of ML and generative AI in assignment and eligibility.
Do you have the right mix of ingredients: (Requirements)
- Master’s degree in data science or related field
- 5+ years of experience in a data science role
- Strong technical foundation across both data science and data engineering — this role owns and directs production pipelines, not just analysis.
- Proven experience designing and leading large-scale data processing systems (hundreds of millions to billions of records), including batch architecture, partitioning, staging, and performance optimization.
- Track record designing activity-based segmentation and tiering frameworks (e.g., RFM-style models, engagement tiers, merchant activity classifications) — from threshold definition through refresh cadence and validation against business outcomes.
- Hands-on background building scoring, ranking, or recommendation frameworks, with feature selection, weighting strategies (rule-based, heuristic, or ML-driven), and evaluation against business objectives; experience evolving such systems from deterministic scoring toward ML-based personalization.
- Experience designing and managing customer segmentation pipelines and feature generation at scale, including the lifecycle management of member groups, derived attributes, and reusable feature sets.
- Experience with workflow orchestration (Airflow/MWAA or equivalent) and AWS data services (S3, Glue, Aurora/PostgreSQL).
- Strong SQL and Python skills — able to review, guide, and produce production-quality data pipeline code.
- Understanding of event-driven architectures and Kafka-based data replication patterns.
- Exper