About Careertakes

👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, Information Services.

Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.

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Role summary

This Data Scientist position partners with product, engineering, UX, analytics, and domain experts to design and ship ML/NLP and generative AI solutions that help researchers and clinicians find, interpret, and apply scientific knowledge. The role focuses on production-ready systems (search, retrieval, ranking, recommendation, question-answering, RAG/LLMs) and building reliable evaluation and monitoring frameworks.

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What You’ll Do

• Design and deploy machine learning, NLP, and generative-AI solutions that improve discovery and understanding of scientific and clinical knowledge.
• Build intelligent retrieval, search, recommendation, ranking, and question-answering systems to improve research outcomes.
• Integrate large language models (LLMs) and retrieval-augmented generation (RAG) into production services.
• Fine-tune, evaluate, and monitor model performance, reliability, and trustworthiness in production.
• Create robust evaluation frameworks and metrics for relevance, accuracy, and user impact.
• Build scalable data pipelines and ML workflows to support experimentation, deployment, and continuous improvement.
• Apply a mix of classical ML, deep learning, retrieval techniques, and generative AI to complex domain problems.
• Collaborate across engineering, product, UX, analytics, and subject-matter experts to turn ambiguous problems into practical solutions.

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Minimum Qualifications

• Degree in Data Science, Machine Learning, AI, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline (Bachelor’s required; advanced degree welcomed).
• Strong Python programming skills and experience delivering production-quality data science solutions.
• Solid grounding in machine learning fundamentals: model development, evaluation, feature engineering, and performance optimization.
• Experience with NLP, information retrieval, or generative-AI model development and evaluation.
• Experience integrating or fine-tuning LLMs and building retrieval-augmented generation (RAG) systems.
• Ability to work on-site regularly in Philadelphia, PA.
• Must be currently authorized to work in the U.S. without employer sponsorship.

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Preferred / Nice-to-Have

• Recent Masters or PhD in a relevant field (encouraged, especially for early-career candidates).
• Experience with search, recommendation, ranking, or question-answering systems.
• Familiarity with knowledge graphs, scientific ontologies, or citation/dataset linking.
• Experience with model monitoring, A/B testing, and ML engineering best practices (CI/CD for ML, feature stores, orchestration).
• Prior experience in health- or research-focused products or information services.

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What You’ll Get / Why This Role

• Structured training and development across NLP, ML, RAG/LLMs, and production ML practices.
• Opportunity to work on high-impact products that improve research and clinical decision-making.
• Clear career progression pathways within the Data Science organization.
• Collaborative, cross-functional teams and opportunities to influence product direction.

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Location & Work Authorization

This role is based in Philadelphia, PA and requires regular on-site work in that location. Applicants must be currently authorized to work in the United States without employer sponsorship; confidential client is not able to sponsor visas for this role.

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Equal Opportunity & Hiring Transparency

Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

If you have questions about how your data is used, please contact us directly.