Senior Data Science Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Take ownership of the entire lifecycle — from the ingestion of raw data to model deployment and assessing real-world business impact.

• Conduct research, prototype, and create ML and LLM-based models to address intricate business challenges.

• Package models into production-ready APIs and seamlessly integrate them into our core product.

• Ensure the outputs of models are interpretable — converting predictions into actionable reason codes for end users.

• Collaborate directly with operational teams to collect feedback, refine features, and enhance model relevance over time.

• Design, construct, and maintain scalable pipelines to ingest data from various sources into our data warehouse/lake.

• Implement robust data validation, quality checks, and transformation workflows across raw, curated, and serving layers.

• Develop and sustain curated datasets optimized for both analytics and model training applications.

• Establish and maintain CI/CD pipelines for both data workflows and ML model deployment across different environments.

• Monitor pipeline latency, data drift, and model performance in production; design alerting mechanisms and retraining triggers.

• Take responsibility for the business outcomes of your models — define success metrics, track ROI, and iterate based on real-world effectiveness.

• Manage infrastructure as code and containerized deployments to ensure reproducible, environment-consistent releases.

⛳️ Requirements

• 5–8+ years of experience in data engineering and data science/ML, with a proven record of delivering models to production.

• Strong proficiency in Python; familiarity with Spark/PySpark for large-scale data processing.

• Advanced SQL skills for complex transformation, analysis, and data modeling.

• Practical experience with cloud data platforms such as Databricks or Snowflake.

• Experience with ETL/ELT frameworks — dbt, Lakeflow Declarative Pipelines, Databricks Autoloader, Informatica, or similar.

• Knowledge of ML experiment tracking tools such as MLflow or Weights & Biases.

• Proficiency in DevOps practices: 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: Practical experience with LLMs and Generative AI techniques in a production context (prompt engineering, RAG architectures, fine-tuning, or evaluation frameworks).

• Experience in building or managing ML platforms, feature stores, or model registries.

• Prior experience in risk, compliance, fraud detection, or other high-stakes ML domains.

🏝️ Benefits

• Competitive compensation.

• Flexible work options.

• Professional development opportunities.