Senior Data Science Engineer

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

📋 Description

• Take ownership of the complete lifecycle from the ingestion of raw data to the deployment of models and the evaluation of their real-world business impact.

• Conduct research, prototype, and develop machine learning and large language model-based solutions for intricate business challenges, with an emphasis on risk detection and prioritization.

• Package models into production-ready APIs and seamlessly integrate them into the primary SaaS product.

• Ensure that model outputs are interpretable by converting predictions into actionable reason codes.

• Collaborate with operational teams to collect feedback, enhance features, and boost model relevance.

• Design, construct, and sustain scalable pipelines that ingest data from various sources into the data warehouse/lake.

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

• Create and maintain curated datasets for analytics and model training purposes.

• Implement and uphold CI/CD pipelines for data workflows and the deployment of ML models.

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

• Establish success metrics, monitor ROI, and iterate models based on their real-world effectiveness.

• Oversee infrastructure as code and containerized deployments for consistent releases.

⛳️ Requirements

• 5–8+ years of experience in data engineering and data science/machine learning, with a proven history of deploying models to production.

• Proficient in Python programming.

• Experience using Spark/PySpark for large-scale data processing tasks.

• Advanced SQL skills for complex transformations, analyses, and data modeling.

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

• Familiarity with ETL/ELT frameworks, including dbt, Lakeflow Declarative Pipelines, Databricks Autoloader, Informatica, or similar tools.

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

• Fluency in DevOps practices: Git-based development, branching strategies, CI/CD, infrastructure as code (DABs/Terraform), and Docker.

• Experience with orchestration tools like Databricks Workflows or Apache Airflow.

• Strong plus: hands-on experience with large language models and Generative AI techniques in a production setting (prompt engineering, retrieval-augmented generation architectures, fine-tuning, or evaluation frameworks).

• Strong plus: experience in building or managing ML platforms, feature stores, or model registries.

• Strong plus: prior experience in risk, compliance, fraud detection, or other critical ML domains.

• Visa sponsorship is not available for this position.

• International remote work is not supported.

🏝️ Benefits

• Competitive compensation package.

• Flexible work options available.

• A team that is genuinely invested in your success.