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, Financial Services.

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

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What You’ll Do

As a Data Scientist (Associate) on this team supporting a Confidential Client in the Financial Services sector, you will help design and implement generative-AI solutions that add real business value. Your day-to-day will include:

• Develop and implement components of generative AI solutions (e.g., RAG pipelines, context engineering, prompt optimization) within an established architecture
• Build and test modules that support Retrieval-Augmented Generation (RAG) and integrate model outputs into microservice-based products
• Work with AI engineers and other data scientists to productionize model outputs and ensure reliable integration with existing systems
• Use and learn our stack (LangChain / LangGraph, Azure AI Search, Databricks / Microsoft Fabric, AKS-based infrastructure)
• Run experiments, document results, and iterate on models, prompts, and retrieval strategies to improve quality and relevance
• Participate in agile ceremonies, contribute to team knowledge sharing, and follow software development lifecycle (SDLC) best practices
• Maintain professional conduct and collaborate across teams to meet product goals

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Qualifications

Minimum requirements

• Bachelor’s degree in Computer Science, Data Science, Statistics, or a related quantitative discipline (required)
• Minimum of ~1 year of relevant experience, or equivalent internship/academic project experience in data science, machine learning, or related software engineering work
• Solid Python skills for data analysis and model development
• Foundational understanding of machine learning concepts and familiarity with LLMs
• Experience or exposure to cloud platforms (preferably Azure) and data platforms such as Databricks or Microsoft Fabric
• Familiarity with version control and development tools (GitHub, ADO) and SDLC principles
• Strong analytical problem-solving skills and a collaborative, team-oriented mindset
• Must be authorized to work in the U.S. without employment-based visa sponsorship now or in the future; the Confidential Client will not sponsor applicants for U.S. work visas

Preferred (nice-to-have)

• Hands-on experience with LangChain, LangGraph, or similar LLM orchestration frameworks
• Experience designing or implementing RAG pipelines, context engineering, or advanced prompt engineering
• Familiarity with Azure AI Search, AKS (Azure Kubernetes Service) deployments, or microservice integration
• Coursework, projects, or a Master’s degree in a relevant discipline

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What We Offer

• Opportunity to work on cutting-edge generative AI initiatives and real-world RAG use cases in Financial Services
• Collaborative, learning-focused environment with mentors and cross-functional teams
• Competitive total rewards and benefits (details provided by the Confidential Client to candidates)
• Rolling recruitment: strong candidates will be considered as they apply until the role is filled

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Legal & Compliance Notes

• Applicants must be authorized to work in the United States without employer sponsorship. The Confidential Client will not provide employment-based visa sponsorship for this position.
• This job posting adheres to applicable U.S. federal, state, and local hiring laws. Where required by jurisdictional pay-transparency laws, the Confidential Client provides accessible salary-range information via its careers resources.

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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.