Senior Data Science Engineer

LegitScript

Senior Data Science Engineer at LegitScript developing ML models for risk detection and operational insights. Involves data engineering, MLOps, and many collaborations with operational teams.

Posted 7/23/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in the full lifecycle of machine learning model development, from data ingestion to deployment, ensuring model interpretability and business impact. Proficient in building scalable data pipelines and implementing CI/CD practices for data workflows and ML models.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProficiencyAdvanced SQLCI/CD ImplementationData Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData IngestionModel DeploymentData ValidationETL/ELT FrameworksCloud Data PlatformsData ProcessingModel Performance MonitoringInfrastructure as CodeGenerative AI Techniques

Soft Skills

CollaborationProblem-SolvingCommunication

Tools & Technologies

DatabricksSnowflakeApache AirflowMLflowDockerGitTerraformInformaticaDbtLakeflow

Industry Keywords

Risk ManagementComplianceFraud DetectionData WarehouseData Lake

Tech Stack

Tools & technologies

AirflowApacheCloudDockerETLInformaticaPySparkPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Own the full lifecycle — from raw data ingestion to model deployment to measuring real-world business impact
Research, prototype, and develop ML and LLM-based models to solve complex business problems
Wrap models into production-ready APIs and integrate them into our core product
Ensure model outputs are interpretable — translating predictions into actionable reason codes for end users
Partner directly with operational teams to gather feedback, refine features, and improve model relevance over time
Design, build, and maintain scalable pipelines to ingest data from disparate sources into our data warehouse/lake
Implement robust data validation, quality checks, and transformation workflows across raw, curated, and serving layers
Build and maintain curated datasets optimized for both analytics and model training use cases
Implement and maintain CI/CD pipelines for both data workflows and ML model deployment across environments
Monitor pipeline latency, data drift, and model performance in production; design alerting and retraining triggers
Own the business outcomes of your models — define success metrics, track ROI, and iterate based on real-world efficacy
Manage infrastructure as code and containerized deployments to ensure reproducible, environment-consistent releases

Requirements

What you’ll need

5–8+ years spanning data engineering and data science/ML, with a demonstrated track record of shipping models to production
Strong Python proficiency; experience with Spark/PySpark for large-scale data processing
Advanced SQL for complex transformation, analysis, and data modeling
Hands-on experience with cloud data platforms such as Databricks or Snowflake
Experience with ETL/ELT frameworks — dbt, Lakeflow Declarative Pipelines, Databricks Autoloader, Informatica, or similar
Familiarity with ML experiment tracking tools such as MLflow or Weights & Biases
DevOps fluency: Git-based development, branching strategies, CI/CD, IaC (DABs/Terraform), and Docker
Experience with orchestration tools such as Databricks Workflows or Apache Airflow
Strong Plus: Hands-on experience with LLMs and Generative AI techniques in a production context (prompt engineering, RAG architectures, fine-tuning, or evaluation frameworks)
Experience building or operating ML platforms, feature stores, or model registries
Prior work in risk, compliance, fraud detection, or other high-stakes ML domains.

Benefits

Comp & perks

Competitive compensation
Flexible work options
Professional development opportunities