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About This Role
About Phaedon
*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 at wearephaedon.com.*
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, 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.*
Pay Range:
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.*
Salary Context
This $105K-$155K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Rippling, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($130K) sits 33% below the category median. Disclosed range: $105K to $155K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rippling AI Hiring
Rippling has 19 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer, Data Engineer. Positions span Remote, US, New York, NY, US, San Francisco, CA, US. Compensation range: $60K - $330K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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