Senior Data Scientist

About the Role:

We are looking for a Senior Data Scientist who is a builder, not just a maintainer. This is a high-ownership opportunity for an AI/ML engineer who wants to design and ship the models that power our loyalty platform in production, not just prototype them. You'll build AI/ML capabilities that our SaaS product calls at runtime: fraud detection, personalization, recommendation, and forecasting models served through APIs, not one-off notebooks handed to someone else to productionize.

We're looking for a self-starter who identifies opportunities to apply AI/ML to the product roadmap, proposes the approach, builds it, ships it, and owns it in production. The primary focus of this role is product-embedded model development. There will be some client-facing work; however, it is anticipated to be a small portion of the role.

Essential Duties/Responsibilities:

Product-Embedded Model Development (primary focus):

• Design, build, and own AI/ML models that are directly integrated into and called by our SaaS product in production

• Own the full model lifecycle: problem framing, data/feature design, training, evaluation, deployment as a callable service, and post-deploy monitoring/retraining

• Build and maintain production inference APIs and microservices that serve model predictions to the product with defined latency and reliability SLAs

• Implement and productionize models using AWS Bedrock, SageMaker, and other AWS AI services, going beyond POC into hardened, versioned, production systems

• Develop RAG (Retrieval-Augmented Generation) systems and other LLM-powered features as first-class product capabilities

• Proactively identify where AI/ML can create product differentiation (fraud detection, member behavior prediction, personalization/recommendation, anomaly detection) and bring proposals forward rather than waiting for requirements to be handed down

Cloud Infrastructure &MLOps:

• Build and manage SageMaker training pipelines, model registry, and endpoint deployments, including feature store integration and automated retraining triggers

• Build automation, monitoring, and alerting for production ML systems using Lambda and other AWS services

• Create and maintain Infrastructure-as-Code (Terraform, Pulumi, CloudFormation) for all model and pipeline infrastructure, no manual, undocumented deployments

• Build data pipelines that synthesize complex datasets from multiple sources into model-ready features

• Develop CI/CD pipelines for automated deployment and model versioning; implement model registry and rollback practices

• Implement error-proofing, integration testing, and monitoring/logging for AI systems running in production

Client & Cross-Functional Collaboration:

• Support select client engagements where deep technical model expertise is needed to scope or validate an AI/ML approach

• Partner with product and analytics leadership to translate roadmap priorities into shipped model capabilities

• When client-facing, present technical findings and recommendations with clarity to both technical and business stakeholders

Location:

This role is based out of our office in the Designer’s Guild building in the heart of Minneapolis' North Loop neighborhood.  We embrace a hybrid model with three in-office days per week to ensure a mix of collaboration and flexibility to support our employees' success.

Basic Qualifications:

• Bachelor's degree in data science, computer science, computer engineering, or related field AND 5+ years of hands-on experience building and shipping ML models into production systems OR equivalent combination of education and experience

• Demonstrated track record of taking a model from idea to production-serving endpoint inside a live product, not just research/POC work; be prepared to speak to specific systems you built that are running in production today

• Fluency in the full model lifecycle: data/feature engineering, training, evaluation, deployment, versioning, monitoring, and retraining

• Knowledge of Infrastructure-as-Code (Terraform, Pulumi, CloudFormation) for deploying ML infrastructure repeatably

• Experience with source control and automated deployment pipelines (Git, Docker)

• A demonstrated self-starter mindset: comfortable identifying a product opportunity, scoping the technical approach, and driving it to completion with minimal guidance

• Strong written and verbal communication skills to document and present technical approaches to engineering and product stakeholders

Technical Skills:

• Programming: Advanced Python (including AI/ML libraries like transformers, LangChain), SQL, Boto3 

• AI/ML Tools: AWS Bedrock, SageMaker, prompt engineering, model fine-tuning 

• Cloud Services: AWS services, particularly Bedrock, SageMaker, Lambda, Redshift, Athena, and Glue

• Visualization: Experience with Superset, Tableau, and/or Power BI

• Development Practices: Object-oriented programming, testing frameworks, CI/CD, model versioning

Preferred Skills:

• Direct experience building models that are embedded in and called by a live SaaS product (recommendation engines, fraud/anomaly detection, personalization, forecasting, chatbots)

• Experience with vector databases and RAG implementations in production

• Knowledge of LLM fine-tuning, evaluation, and deployment strategies at scale

• Strong MLOps background: model versioning, automated retraining, drift detection, canary/shadow deployments

• Experience with API development and microservices architecture in a product engineering context

• Background in fraud detection, loyalty/rewards platforms, or marketing/AdTech modeling a plus

• Prior experience balancing product engineering with occasional client-facing technical work

What we Offer:

We value our employees and demonstrate this through our comprehensive benefits offering including medical/dental/vision coverage, comprehensive paid time off, paid holidays, paid parental leave, retir