Principal Data Scientist

Role Overview

• Architect and implement end-to-end ML systems (data pipelines, feature engineering, model training, deployment, and monitoring).
• Design scalable, low-latency model serving infrastructure integrated with Kubernetes and cloud-native systems.
• Build intelligent automation solutions including predictive autoscaling, anomaly detection, seasonality-aware forecasting, and capacity optimization.
• Engineer safe and reliable ML-driven automation that operates in high-availability environments.
• Own model lifecycle management, including validation, experiment tracking, model registry, monitoring, drift detection, and rollback strategies.
• Collaborate closely with platform, SRE, and infrastructure teams to embed ML capabilities into production systems.
• Drive engineering best practices around ML system reliability, observability, testing, and performance.
• Contribute to architectural decisions and mentor engineers on ML systems design.

Requirements

• Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 5 years' experience in an analytics related field.
• Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 3 years' experience in an analytics related field.
• Option 3: 7 years' experience in an analytics or related field.
• Strong proficiency in one or more programming languages commonly used in ML engineering, such as Python, Go, or Java.
• Strong experience with ML frameworks such as Scikit-learn, PyTorch, TensorFlow, or similar.
• Strong SQL skills and experience working with large-scale datasets.
• Hands-on experience training, validating, and deploying machine learning models in production across domains such as recommendation systems, forecasting, anomaly detection, classification, or similar applied ML use cases.
• Experience building and maintaining end-to-end ML pipelines (data ingestion, feature engineering, training, evaluation, deployment, monitoring).
• Experience with model serving architectures (REST/gRPC inference services, batch inference, streaming inference).
• Hands-on experience with ML lifecycle platforms such as MLflow, Ray, Kubeflow, Airflow, or similar orchestration systems.
• Experience with experiment tracking, model registry, CI/CD for ML, feature management, and automated retraining workflows.
• Experience designing robust evaluation frameworks for traditional ML systems (offline validation, backtesting, shadow testing, A/B testing, and production performance monitoring).
• Strong experience working with observability data (metrics, logs, traces) and time-series analysis in distributed systems.
• Hands-on experience deploying and operating ML systems on Kubernetes, including containerization using Docker.
• Experience working with major cloud platforms (AWS, GCP, or Azure) and cloud-native services.

Tech Stack

• Airflow
• AWS
• Azure
• Cloud
• Distributed Systems
• Docker
• Google Cloud Platform
• GRPC
• Java
• Kubernetes
• Python
• PyTorch
• Ray
• Scikit-Learn
• SQL
• Tensorflow
• Go

Benefits

• Health benefits include medical, vision and dental coverage.
• Financial benefits include 401(k), stock purchase and company-paid life insurance.
• Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting.
• Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more.
• You will also receive PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes. The amount you receive depends on your job classification and length of employment.