Senior Data Scientist

6+ years

Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, or a closely related quantitative discipline.
• Five or more years of professional experience developing production-grade machine learning models, predictive analytics solutions, and enterprise data science applications.
• Strong, demonstrable understanding of statistics, probability, machine learning algorithms, data structures, data modeling, feature engineering, model evaluation techniques, and end-to-end machine learning lifecycle principles.
• Advanced working knowledge of Python, R, SQL, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, and modern data science libraries for building scalable analytical solutions.
• Hands-on, production-level experience designing, training, validating, deploying, and monitoring machine learning models, including regression, classification, clustering, forecasting, recommendation systems, and natural language processing applications.
• Proven experience working with relational and NoSQL databases, large-scale datasets, data warehouses, and distributed data processing platforms such as Spark, Hadoop, Snowflake, Databricks, or BigQuery.
• Strong SQL skills and meaningful experience performing data exploration, feature engineering, query optimization, ETL development, data visualization, and business intelligence reporting using enterprise data platforms.
• Solid experience with Git-based version control workflows, CI/CD processes, MLOps practices, model deployment pipelines, code review processes, and collaborative software development methodologies.
• Hands-on experience deploying machine learning solutions on at least one major cloud platform (AWS, Azure, or GCP), including managed AI/ML services, storage, networking, and identity management capabilities.
• Strong debugging, analytical thinking, problem-solving, and root-cause analysis skills, with the discipline to investigate complex data challenges methodically, communicate findings effectively, and translate analytical insights into actionable business recommendations.
• Experience designing and deploying real-time machine learning systems, recommendation engines, streaming analytics, event-driven architectures, or large-scale AI applications using Kafka, Spark Streaming, or equivalent technologies.
• Familiarity with containerization and orchestration using Docker, Kubernetes, Kubeflow, MLflow, Airflow, or equivalent platforms for production machine learning operations.
• Exposure to advanced artificial intelligence concepts such as deep learning, reinforcement learning, computer vision, generative AI, large language models (LLMs), explainable AI (XAI), model fairness, and responsible AI practices.
• Experience implementing automated testing, model monitoring, feature stores, experiment tracking, data governance, MLOps best practices, and continuous machine learning delivery pipelines within enterprise Agile software development environments.