Data Scientist
Omni Inclusive
Sr. Data Scientist profiles with Gen AI exp.
Azure Cloud and Snowflake
Azure Cloud and Snowflake
- Experience within the Azure ecosystem (including Azure AI Search, Azure Storage Blob, Azure Postgres, and Azure SQL) with expertise in leveraging these tools for data processing, storage, AI models, and analytics tasks.
- Experience within the Snowflake ecosystem (including Snowflake data warehouse, Snowpark, Snowflake Notebooks, and Streamlit) with expertise in leveraging these tools to create data and AI pipelines.
- Knowledge of cloud computing principles and experience in deploying, scaling, and monitoring AI solutions on cloud platforms like Azure.
- Knowledge of cloud cost management principles and best practices to optimize cloud resource usage and minimize costs.
- A solid foundation in Python programming is essential, along with familiarity with popular AI libraries/frameworks such as Langchain, TensorFlow, PyTorch, scikit-learn, and spaCy.
- Demonstrated proficiency in developing AI solutions using Azure services, including Azure AI Search, Azure OpenAI APIs, and Azure SQL Database.
- In-depth knowledge of search algorithms, indexing techniques, and retrieval models for effective information retrieval tasks. Familiarity with search platforms like Elasticsearch or Azure AI Search is highly advantageous.
- Hands-on experience with RAG architecture and its application in natural language processing tasks is a must.
- Ability to proficiently manipulate data, perform complex queries, and conduct data transformations using Microsoft SQL Server.
- A solid understanding of machine learning and deep learning techniques, particularly those relevant to natural language processing tasks such as text extraction, text classification, sentiment analysis, named entity recognition, and summarization.
- Ability to preprocess and clean large datasets efficiently using SQL/Python and other data manipulation tools. Experience with techniques such as data normalization, feature engineering, and data augmentation is preferred.
- Required : Familiarity with open-source AI Agentic frameworks (Crew.ai, Agno, Microsoft Auto-Gen, browser-use)
- Basic familiarity with (but not proficiency in) alternative AI programming languages (Julia, Haskell, R, Scala, C++)
- Demonstrated experience in productionalizing AI models that have been developed including end-to-end deployment and integration into business systems. Delivers AI models to the last mile. Does not have a "code it and let someone else deploy it" mentality.
- Demonstrated experience in integrating AI models into business systems such as Salesforce, PowerBI, etc.
- Expertise in healthcare data standards, such as HIP nd FHIR, with a deep understanding of sensitive data handling and data masking techniques to protect PII and PHI
- Ability to implement model governance practices to ensure compliance and auditability of AI/ML system.
- Ability to liaise with non-technical business stakeholders to ask them questions, provide updates and collect requirements
- Minimum of 10 years' experience as a data scientist.
- Demonstrated track record of deploying multiple AI models into production and demonstrated track record of quantifying business impact from the models
- Deep hands-on experience using Python (or R, Julia, Haskell, or Scala).
- Hands-on experience scaling POC to production
- Hands-on experience with Azure Cloud eco-system
- Hands-on experience using tools such as Azure Document Intelligence (formerly Azure Form Recognizer), Snowflake, Azure AI Search, Azure OpenAI, Azure AI Foundry
- Experience working with PII/PHI
- Hands-on experience working with unstructured data.
- Must have implemented a use case using RAG architecture.
Vacancy posted 1 day ago
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