Data Scientist - AI [T500-27599]

Talent500 is hiring for one of its clients.

Role Overview :

We are seeking a versatile and forward-thinking Data & AI Analyst to join our team. This role bridges the gap between traditional data analytics and modern Generative AI implementation. You will be responsible for parsing highly unstructured documents (including complex Excel workbooks), leveraging Advanced Language Models (LLMs) to drive automation and insights and handling large-scale datasets,

We value strong, adaptable engineering fundamentals and problem-solving skills over platform-specific certifications.

Key Responsibilities

• Unstructured Document Parsing: Extract clean, structured insights from a wide variety of complex files—including PDFs, multi-tab Excel workbooks, financial models, PDFs, images, and scanned forms—with varying levels of data quality and layout consistency.
• Tool Selection & Strategy: Exercise sharp technical judgment to evaluate business problems and select the optimal technology mix—knowing when to use standard data manipulation libraries, a cloud OCR tool, or a heavyweight LLM.
• Business Application Integration: Help bridge the gap between standalone AI models and our core business applications, ensuring extracted data and AI insights flow smoothly into internal workflows and systems.
• Automated Report Writing: Design and implement systems that automate the creation of data-driven reports, summaries, and executive briefs, combining structured data analytics with GenAI text generation.
• Data Management & Processing: Query, transform, and manage large datasets within cloud data warehouses. Work seamlessly with optimized data storage formats (e.g., Parquet) to ensure high-performance data pipelines.
• AI & RAG Architecture: Design, build, and maintain Retrieval-Augmented Generation (RAG) systems to ground LLMs in internal company data, utilizing tools like Azure Document Intelligence for upstream data extraction.
• Collaboration & Code Hygiene: Maintain clean, documented, and reusable codebases. Participate in team workflows using standard Git version control practices.

Required Skills & Qualifications:

Artificial Intelligence & Document Parsing:

• Advanced Document Extraction: Hands-on experience extracting data from messy, semi-structured, or completely unstructured files, with specific experience programmatically parsing or chunking complex Excel/spreadsheet data and PDFs.
• Extraction Tools: Familiarity with Azure Document Intelligence or equivalent advanced layout extraction tools.
• RAG Frameworks: Practical experience building Retrieval-Augmented Generation systems (e.g., working with vector databases and frameworks like LangChain or LlamaIndex).
• Model Selection: Conceptual and practical familiarity with the AI ecosystem, including commercial APIs (OpenAI, Anthropic, Gemini) and lightweight/open-source models (Llama, Mistral, Phi).

Integration & Software Engineering:

• Systems Key Responsibilities
• Unstructured Document Parsing: Extract clean, structured insights from a wide variety of complex files—including PDFs, multi-tab Excel workbooks, financial models, PDFs, images, and scanned forms—with varying levels of data quality and layout consistency.
• Tool Selection & Strategy: Exercise sharp technical judgment to evaluate business problems and select the optimal technology mix—knowing when to use standard data manipulation libraries, a cloud OCR tool, or a heavyweight LLM.
• Business Application Integration: Help bridge the gap between standalone AI models and our core business applications, ensuring extracted data and AI insights flow smoothly into internal workflows and systems.
• Automated Report Writing: Design and implement systems that automate the creation of data-driven reports, summaries, and executive briefs, combining structured data analytics with GenAI text generation.
• Data Management & Processing: Query, transform, and manage large datasets within cloud data warehouses. Work seamlessly with optimized data storage formats (e.g., Parquet) to ensure high-performance data pipelines.
• AI & RAG Architecture: Design, build, and maintain Retrieval-Augmented Generation (RAG) systems to ground LLMs in internal company data, utilizing tools like Azure Document Intelligence for upstream data extraction.
• Collaboration & Code Hygiene: Maintain clean, documented, and reusable codebases. Participate in team workflows using standard Git version control practices.

Required Skills & Qualifications:

Artificial Intelligence & Document Parsing:

• Advanced Document Extraction: Hands-on experience extracting data from messy, semi-structured, or completely unstructured files, with specific experience programmatically parsing or chunking complex Excel/spreadsheet data and PDFs.
• Extraction Tools: Familiarity with Azure Document Intelligence or equivalent advanced layout extraction tools.
• RAG Frameworks: Practical experience building Retrieval-Augmented Generation systems (e.g., working with vector databases and frameworks like LangChain or LlamaIndex).
• Model Selection: Conceptual and practical familiarity with the AI ecosystem, including commercial APIs (OpenAI, Anthropic, Gemini) and lightweight/open-source models (Llama, Mistral, Phi).

Integration & Software Engineering:

• Systems Integration: Strong experience consuming and building RESTful APIs to connect AI models, data systems, and downstream business applications.
• Version Control: Proficient with Git and standard platform workflows (GitHub, GitLab, or Azure DevOps).

Data & Analytics Infrastructure:

• Big Data Handling: Comfortable working with large-scale datasets and optimized data formats (specifically JSON, CSV, and Parquet).
• Data Pipelines: Strong proficiency in manipulating data via Python and writing clean, efficient SQL.

Preferred / Nice-to-Have Qualifications:

• Experience working within the Azure cloud ecosystem and running queries in Snowflake.
• Automated Report Generation: Experience building systems that automatically synthesize data into natural language reports or automated client-facing summaries.
• Familiarity with prompt engineering techniques and LLM evaluation frameworks.n: Strong experience consuming and building RESTful APIs to connect AI models, data systems, and downstream business applications.
• Version Control: Proficient with Git and standard platform workflows (GitHub, GitLab, or Azure DevOps).

Data & Analytics Infrastructure:

• Big Data Handling: Comfortable working with large-scale datasets and optimized data formats (specifically JSON, CSV, and Parquet).
• Data Pipelines: Strong proficiency in manipulating data via Python and writing clean, efficient SQL.

Preferred / Nice-to-Have Qualifications:

• Experience working within the Azure cloud ecosystem and running queries in Snowflake.
• Automated Report Generation: Experience building systems that automatically synthesize data into natural language reports or automated client-facing summaries.
• Familiarity with prompt engineering techniques and LLM evaluation frameworks.