Principal Data Scientist

About the Role

Key Responsibilities

• Demonstrate strong experience in cloud technologies with a particular focus on Snowflake.
• Architect end-to-end integration between enterprise platforms (e.g., ServiceNow, Salesforce, Workday) and Snowflake.
• Design and develop AI agents leveraging Large Language Models (LLMs) across various cloud platforms.
• Enable data exchange between Snowflake and AI agents for inference and analytics.
• Build and deploy machine learning models and NLP pipelines, including deep learning architectures such as CNNs, RNNs, and Transformers.
• Preprocess large datasets for training and inference using scalable pipelines.
• Build robust data pipelines and ensure efficient data flow across systems.
• Utilize solid Python programming skills for data extraction and ML integration.
• Deploy ML models using containerization and orchestration tools such as Docker and Kubernetes on AWS, GCP, or Azure.
• Ensure scalable and production-grade model deployment environments.
• Integrate and visualize data from Snowflake and cloud applications into dashboards for business intelligence and operational efficiency.
• Conduct deep analysis and interpretation of complex datasets to inform high-level business decisions.
• Collaborate with stakeholders to define AI/ML use cases and roadmap.
• Drive the roadmap and vision for ML and AI capabilities aligned with customer and business goals.
• Mentor junior team members and promote best practices in data science and architecture.
• Work closely with cross-functional teams to embed AI into enterprise systems.

Requirements

• Experience in cloud technologies, especially Snowflake.
• Knowledge of enterprise platform integration (e.g., ServiceNow, Salesforce, Workday).
• Hands-on experience in building and deploying ML models and NLP pipelines.
• Experience with deep learning architectures including CNNs, RNNs, and Transformers.
• Proficiency in building scalable data pipelines and data processing workflows.
• Strong Python programming skills for data extraction and ML tasks.
• Experience deploying ML models using containerization and orchestration tools such as Docker and Kubernetes.
• Familiarity with cloud platforms like AWS, GCP, or Azure.
• Ability to analyze complex datasets and derive insights for business decisions.
• Excellent collaboration and stakeholder communication skills.

Nice to Have

• Experience with Knowledge Graphs and Graph Machine Learning.
• Knowledge of AI integration with large language models (LLMs).
• Experience with data visualization tools and dashboard development.
• Understanding of enterprise architecture and data integration strategies.

Benefits & Perks

• Discretionary annual incentive program and stock awards based on performance.
• Salary between $150,000 and $193,000 depending on experience and qualifications.
• Medical, Dental, Vision, and Life Insurance.
• Paid holidays and Paid Time Off.
• 401(k) plan and contributions.
• Long-term and Short-term Disability.
• Paid Parental Leave.
• Employee Stock Purchase Plan.