Senior Data Modeler Jobs in Minnesota (NOW HIRING)

Phaedon is a leading loyalty partner for organizations across travel, hospitality, and retail, helping brands humanize loyalty by transforming customer interactions into meaningful, lasting relationships. Through innovative technology, strategic expertise, and advanced analytics, Phaedon delivers end-to-end loyalty solutions that generate measurable business outcomes. Its award-winning Tally™ platform enables organizations to scale loyalty with precision, deepen customer engagement, and strengthen connection at every stage of the relationship. Trusted by leading and growth-focused brands, Phaedon helps clients turn loyalty into a sustained driver of growth and long-term competitive advantage. Learn more atwearephaedon.com.

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.

• 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

• 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

• 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

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.

• 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

• 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

• 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

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, retirement savings plans, and more.

Please note that the company does not offer sponsorship of employment visas for this role (e.g., H1B, 0-1, TN, CPT, OPT, etc.). To be considered for this opportunity, candidates must be currently authorized to work in the United States on a permanent, unrestricted basis.

The pay range for this position is estimated to be: $105,000-155,000 per year.

There are multiple factors that are considered in determining final pay for a position, including, but not limited to, relevant work experience, skills, certifications and competencies that align to the specified role, geographic location, education and certifications as well as contract provisions regarding labor categories that are specific to the position.

Phaedon is an equal opportunity employer, and all employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process.

The statements contained in this job description reflect general details as necessary to describe the principal functions of this job, the level of knowledge and skill typically required and the scope of responsibility. It should not be considered an all-inclusive listing of work requirements. Individuals may perform other duties as assigned, including work in other functional areas to cover absences, to equalize peak work periods, or to otherwise balance organizational workload.