Senior Data Science Engineer

At LegitScript, we are passionate about making the internet and payment ecosystems safer and more transparent. We help companies of all sizes keep their services legal and safe for consumers. To do this, LegitScript combines big data with the world’s leading team of experts skilled in highly regulated and complex sectors, including transaction laundering detection, pharmaceuticals, online gambling, and more.

The result? Unmatched accuracy and deep risk analysis that identifies which commercial entities play by the rules, and which do not. Our diverse industry partnerships provide unique insights that keep businesses and governments at the forefront of emerging trends. That’s why LegitScript is trusted by the world's largest search engines, internet platforms, payment companies, and regulatory agencies.

You’ll own the full lifecycle - from raw data ingestion to model deployment to measuring real-world business impact - with a current focus on building a sophisticated risk detection system using LLMs, Generative AI techniques, and classical ML within our SaaS platform. This is not a pure engineering role or a pure research role. You’ll need both, and you’ll need to move fluidly between them.

• Research, prototype, and develop ML and LLM-based models to solve complex business problems, with a current focus on risk detection and prioritization
• 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

• 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
• Dev Ops fluency:
  Git-based development, branching strategies, CI/CD, IaC (DABs/Terraform), and Docker
• Experience with orchestration tools such as Databricks Workflows or Apache Airflow

• 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

This job description is not designed to cover or contain a comprehensive listing of all activities, duties or responsibilities that are required of the employee. Duties, responsibilities and activities may change or new ones may be assigned at any time with or without notice.

Please note that visa sponsorship is not available for this position. We cannot support international remote work.

The pay range for this role is:

160, USD per year (Remote (United States))