Data Scientist

Overview

Placement Type:
Temporary

Salary: $ Hourly

Start Date:

Jan 12, 2026

Aquent has an exciting opportunity for a Data Scientist!

While this position is fully remote, we will only consider candidates who currently live within the United States. Out-of-country candidates will not be considered.

In addition, to be considered for this role, you must:

• Be authorized to work in the United States
• Not require sponsorship of any kind for the duration of the assignment
• Be able to work on a W-2 basis. C2C or 1099 is not permitted for this role

This is an exciting opportunity to join a leading organization dedicated to transforming the way services are delivered, focusing on innovation and data-driven strategies. This company is at the forefront of leveraging advanced technologies to enhance decision-making and optimize operations, making a significant impact on the industry. As a key contributor, you will play a pivotal role in shaping the future of analytics and strategic initiatives by building cutting-edge, AI-powered solutions that directly influence growth and competitive advantage.

Your work will not only drive technological advancement but also directly contribute to the company's mission of improving outcomes through intelligent data utilization.

We are seeking a visionary individual to lead the development and deployment of sophisticated AI capabilities. In this role, you will be instrumental in creating advanced systems that can intelligently process vast and diverse datasets, extracting critical insights from complex documents. You will design, build, and maintain robust data pipelines and information extraction workflows, directly impacting strategic analytics and decision-making processes.

This is a chance to apply your expertise in large language models and data engineering to solve real-world challenges, delivering verifiable answers and actionable intelligence that will shape the company's future direction.

• Architect, build, and refine retrieval-grounded LLM systems, including advanced RAG patterns, to deliver grounded, verifiable answers and insights.
• Design robust pipelines for ingestion, transformation, and normalization of public and internal data, including ETL, incremental processing, and data quality checks.
• Build and maintain document processing workflows across various formats like PDFs, HTML, and scanned content, incorporating OCR, layout-aware parsing, table extraction, metadata enrichment, and document versioning.
• Develop information extraction pipelines using LLM methods and best practices, including schema design, structured outputs, validation, error handling, and accuracy evaluation.
• Own the retrieval stack end-to-end, encompassing chunking strategies, embeddings, indexing, hybrid retrieval, reranking, filtering, and relevance tuning across vector databases or search platforms.
• Implement web data acquisition where necessary, including scraping, change detection, source quality checks, and operational safeguards like retries and rate limiting.
• Establish evaluation and monitoring practices for retrieval and extraction quality, including golden datasets, regression testing, groundedness checks, and production observability.
• Collaborate with subject matter experts to translate business needs into practical retrieval and extraction workflows and measurable success criteria.
• Communicate complex findings, tradeoffs, and recommendations to technical and business stakeholders, supporting data-driven forecasting and strategy.
• Ensure compliance with data governance and security standards when handling sensitive data and deploying systems to production environments.

• Advanced degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field.
• Minimum of 4 years of experience in data science or applied ML/NLP with a strong focus on NLP & Generative AI.
• Proficiency in Python and SQL, coupled with strong engineering practices for building maintainable, testable pipelines.
• Strong experience with Databricks for data processing and pipeline development, including Spark and common lakehouse patterns.
• Demonstrated experience building retrieval-grounded LLM systems or LLM-based information extraction solutions for real-world use cases.
• Experience with document ingestion and parsing, including OCR and handling messy, semi-structured content such as PDFs, tables, forms, and web pages.
• Familiarity with vector databases and retrieval concepts, including indexing, embeddings, hybrid retrieval, reranking, and performance and cost tuning.
• Strong understanding of best practices for reasoning models and techniques that improve reliability and reduce hallucinations, including grounding and attribution.
• Excellent communication skills, with a proven track record of partnering with stakeholders and transforming ambiguous requests into adopted solutions.
• Proficiency with LLM and orchestration libraries such as openai, google-genai, langgraph, and langchain.
• Experience with…