Senior Data Scientist - Core Machine Learning

About the role

Artmac Soft is a technology consulting and service-oriented IT company that provides innovative technology solutions and services to customers. We are seeking a hands-on Senior Data Scientist to architect, develop, and productionize enterprise-scale machine learning solutions. This client-facing role requires deep expertise in feature engineering, ML architecture, end-to-end pipelines, model routing, and production observability, along with the ability to translate complex business requirements into scalable ML solutions.

Job Type: W2/C2C | Experience: 8-12 Years | Location: San Jose, California (On-Site)

Responsibilities

• Design and develop predictive, classification, recommendation, ranking, and deep learning models, selecting appropriate architectures based on business and technical requirements.
• Lead feature engineering, exploratory data analysis, statistical validation, experimentation, and model performance optimization.
• Own end-to-end ML pipelines, including data ingestion, training, deployment, monitoring, and automated retraining.
• Design model routing and selection strategies, including embedding-based routing, confidence thresholds, fallback mechanisms, and output validation.
• Implement production observability, drift detection, automated recovery, alerting, and model rollback strategies.
• Collaborate directly with client stakeholders and cross-functional teams to define ML solutions, communicate architectural decisions, and drive production delivery.
• Mentor junior data scientists and contribute to ML engineering best practices.

Requirements

• 8+ years of hands-on experience in applied machine learning and data science, with proven ownership of production ML solutions.
• Strong expertise in feature engineering, ML algorithms, model architecture, statistics, experimentation, and A/B testing.
• Advanced Python and SQL proficiency, with experience in scikit-learn and PyTorch or TensorFlow.
• Experience with large-scale data processing using Spark/PySpark and cloud ML platforms such as AWS SageMaker, Azure ML, or Google Vertex AI.
• Hands-on experience with MLOps, MLflow or equivalent, Docker, Kubernetes, CI/CD and model lifecycle management.
• Experience implementing model routing, fallback logic, observability, drift monitoring, and automated recovery.
• Strong communication skills and experience presenting technical solutions to client stakeholders.

Qualifications

Bachelor's degree or equivalent combination of education and experience.