Senior Data Science Engineer
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Job Description
Sr Data Science Engineer at LegitScript developing ML models for risk detection and prioritization. Collaborating on data pipelines and production-ready APIs in a flexible work environment.
Responsibilities:
- Data Science & Applied ML: 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
- Data Engineering: 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
- MLOps & Production Ownership: 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:
- 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:
- Competitive compensation
- Flexible work options
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