Staff Data Scientist, Technical Lead – Full-Stack, Production ML

Appriss Retail

. Own end-to-end delivery of high-impact data science projects from ambiguous business request to production-ready system .

Posted 10/9/2026full-timeRemote • United StatesSenior💰 $160,000 - $170,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in end-to-end data science project delivery, including data pipeline design, ML model development, and cloud infrastructure management. Proficient in Python and SQL, with a strong foundation in software engineering practices and team leadership.

Highest-signal resume keywords

Expert-Level SQLExpert-Level PythonCloud Data Platform ExperienceData Pipeline DesignML Model Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingData GovernanceML Model EvaluationVersion ControlCI/CD PipelineInfrastructure-As-CodeContainerizationPrompt EngineeringAnalytical Project ScopingTechnical Leadership

Soft Skills

CommunicationMentoringCollaborationInfluencing Decisions

Tools & Technologies

SnowflakeAzureAWSGCPTerraformCloudFormationDockerKubernetesMLflowAirflow

Certifications & Qualifications

Master's DegreeBachelor's Degree

Industry Keywords

Data ScienceData EngineeringRetailFraud DetectionTransaction-Level Data

Tech Stack

Tools & technologies

AirflowAWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Own end-to-end delivery of high-impact data science projects from ambiguous business request to production-ready system
Design and maintain data pipelines, data models, and governance standards
Build, evaluate, and iterate on ML models in production
Lead experimentation rigor, monitoring, and lifecycle management
Architect and ship LLM-integrated features and agentic workflows, including prompt engineering, tool use, and output evaluation
Guide cloud infrastructure architecture for data science projects with attention to performance, maintenance, and cost
Write production-grade Python and SQL, enforce code review practices, and maintain documentation
Partner with engineering to integrate models and pipelines into core product infrastructure
Translate ambiguous business problems into scoped analytical and modeling work with defined success criteria
Partner with product, engineering, and business stakeholders
Contribute to the data and analytics roadmap
Communicate technical findings and influence decisions with data and model outputs
Lead a small data science team as a player-coach
Set technical direction and mentor the team, with potential to grow into direct management
Report to the Director of Data Science
Travel up to 10% for training and conferences

Requirements

What you’ll need

6+ years of experience in data science, data engineering, or a closely related technical discipline
1+ year of formal technical lead experience over a team
Expert-level SQL and Python; production code, not just analysis scripts
Deep understanding of data infrastructure: pipelines, warehousing, data modeling, and source system behavior
Strong software engineering practices: version control, code review, testing, and CI/CD pipeline experience for a data or ML workload
Ability to scope and deliver complex analytical projects independently from vague inputs
Cloud data platform experience with Snowflake, Azure, AWS, or GCP
Working knowledge of infrastructure-as-code such as Terraform or CloudFormation
Containerization experience with Docker, ideally Kubernetes or a managed container service
Experience making and defending build-vs-buy or cost/latency tradeoffs on a production ML or data system
Familiarity with ML platform tooling such as MLflow, feature stores, or model registries
Required Master's degree or Bachelor's Degree in a technical, quantitative field
Preferred: proficiency with dbt, Airflow, Spark, or equivalent
Preferred: demonstrated LLM experience including prompt engineering, RAG, fine-tuning, or agent frameworks
Preferred: familiarity with agentic AI architectures including multi-step reasoning, tool use, memory, and orchestration
Preferred: experience in retail, fraud detection, or transaction-level data at scale

Benefits

Comp & perks

Multiple medical plan options
Dental and vision coverage
Health savings and flexible spending accounts
Paid parental leave
Supplemental coverage for life’s unexpected moments
Generous paid time off
401(k) with immediate vesting and company match
Short- and long-term disability
Free access to health and wellbeing resources such as Calm and Sworkit
Learning and development opportunities
12–15% bonus in addition to base salary