Sr Data Science Engineer

What You'll Do

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

What You'll Bring

• 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