Data Entry Jobs 2026 (Now Hiring) – Smart Auto Apply

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.
• Experience with or strong understanding of real-time or near-real-time data systems, and the ability to provide architectural guidance even when not the primary builder.
• Ability to define pipeline SLAs and data freshness guarantees, including monitoring, alerting, and incident response for batch and near-real-time workflows.
• Experience working with large-scale member or customer data in a personalization, targeting, loyalty, or recommendation context.
• Demonstrated ability to work cross-functionally and influence without authority; self-directed and able to own a workstream end-to-end with minimal oversight.
• Strong written and verbal communication; able to produce clear documentation and present findings to non-technical audiences.

Nice to Have:

• Experience with offer, loyalty, dining, or hospitality platforms.
• Familiarity with Scala or JVM-based systems, particularly in real-time API or microservice contexts that integrate with data pipelines.
• Experience with analytics engineering (dbt or similar) or oversight of BI data model layers.
• Familiarity with CDC-based replication patterns and data synchronization between systems.
• Familiarity with ML model deployment and serving (AWS SageMaker, Bedrock, or equivalent), A/B testing frameworks, and an informed point of view on how foundation models and RAG-based architectures can be applied to personalization and recommendation at scale.

Tech Stack:

• Aurora (PostgreSQL) / SQL
• Python
• AWS (S3, Glue, Redshift, CloudWatch, MSK)
• Airflow (MWAA)
• Kafka / Kafka Connect
• Scala / Kubernetes - architectural awareness required; not expected to be the primary builder

What you'll love about us: 

Comprehensive benefits package, which includes:

• This is a full-time, exempt position. The base salary range for this role in Chicago is $135,000-$150,000 annualized, depending on level, candidate experience, skills, and other factors. This role is also eligible for an annual bonus target of 10%, bringing total target compensation to $148,500-$165,000.
• Competitive Time Off Benefits: including flexible PTO, 11 company holidays, and parental leave. 
• Generous dining reimbursement when you dine with our restaurant clients 
• 401(k) plan with a company match
• Two medical plan options- Standard PPO or High Deductible Health Plan (HSA with company match for HDHP participants)
• Partnership with Rx n Go, offering certain prescriptions for free
• Two dental plan options and a vision plan
• Flexible Spending Accounts and a pre-tax commuter benefit program
• Accident, Critical Illness, and Hospital Indemnity Insurance Plans
• Short Term and Long Term disability 
• Company-paid life insurance and AD&D insurance, supplemental employee, spouse, and child life insurance 
• Employee Life Assistance Program
• Hybrid working environment in a new office space downtown near the Metra Train stations and catered lunches on Tuesdays. 

Rewards Network is an Equal Opportunity Employer (EOE). We encourage and strongly support workplace diversity.

Expected Pay Range

$135,000—$150,000 USD