Data Scientist, Specialist
AI in this role
About Vanguard
Founded in 1975, Vanguard is one of the world's leading investment management companies. The firm offers investments, advice, and retirement services to tens of millions of individual investors around the globe—directly, through workplace plans, and through financial intermediaries.
Vanguard’s India Office
Vanguard’s office in India is a significant milestone in our global expansion. We are committed to establishing an enduring technology center in Hyderabad, Telangana and are excited to be adding talent who will focus on Artificial Intelligence (AI), mobile, and cloud-based technologies that drive our business outcomes and deliver a world-class experience for our clients.
Role Summary
As a Senior Data Scientist, you will lead the design, governance, and deployment of enterprise AI agents that automate knowledge-intensive business processes by combining LLMs, enterprise data platforms, and agentic workflows. Partners with business, risk, compliance, and technology stakeholders to transform operations through trusted, scalable, and governed AI solutions that deliver measurable business outcomes.
Responsibilities
Solve Business Problems with AI
- Design and build enterprise knowledge systems that integrate structured and unstructured data across multiple business platforms, enabling AI agents to retrieve, reason over, and operationalize trusted organizational knowledge.
- Partner with business stakeholders to identify, frame, and prioritize high‑value problems that can be addressed using Agentic AI, LLMs, and NLP.
- Define and implement business-centric evaluation frameworks that measure coverage, relevance, trustworthiness, explainability, and user adoption in addition to technical model performance.
- Focus on business outcomes, not just model performance.
Design & Build Agentic AI Solutions
- Architect and develop agentic AI systems that can reason, plan, and take actions across tools, workflows, and data sources.
- Design multi‑agent and tool‑augmented LLM solutions to automate complex, multi‑step processes.
- Ensure solutions are reliable, explainable, and governed for enterprise use.
Scalable & Responsible AI
- Collaborate with engineering teams to deploy AI solutions with scalability, security, and performance in mind.
- Implement evaluation, monitoring, and guardrails for LLM and agentic systems, including bias, drift, and failure modes.
- Align solutions with enterprise risk management, compliance, and responsible AI standards.
Thought Leadership & Collaboration
- Act as a trusted AI advisor, helping teams understand where Agentic AI and LLMs add value—and where they do not.
- Contribute to AI best practices, reusable patterns, and strategic direction
- Mentor peers and teammates on applied AI and business‑driven problem solving
Qualifications and Skills
- Minimum 8 years of experience in data science, predictive analytics, or advanced statistical modeling roles
- Bachelor’s degree (B.E./B.Tech) in Analytics, Applied Mathematics, Economics, Statistics, or Computer Science, or a master’s degree/Diploma in a related quantitative field
- Agentic AI: Experience designing AI agents that reason, plan, and act across systems.
- Large Language Models (LLMs): Hands‑on experience building enterprise LLM applications (e.g., RAG, tool use, orchestration, evaluation).
- Natural Language Processing (NLP): Strong experience working with unstructured text and language‑driven workflows.
- MS or PhD in Computer Science, Machine Learning, Data Science, or a related quantitative field.
- 3+ years delivering AI/ML solutions in production environments.
- 5+ years of hands‑on Python experience; experience with distributed data processing is a plus.
- Strong ability to solve business problems using AI, not just build models.
- Excellent communication skills, with the ability to explain complex concepts to both technical and non‑technical audiences.
- Experience working in cross‑functional, enterprise environments.
Location
This role is based in Hyderabad, Telangana at Vanguard’s India office. Only qualified external applicants will be considered.
Our mission
Vanguard adheres to a simple purpose: To take a stand for all investors, to treat them fairly, and to give them the best chance for investment success.
Our commitment to you
Vanguard takes the same long-term view of your success—at work and in life—with Benefits and Rewards packages that reflect what you care about, throughout all the phases and stages of your life. Our Total Rewards programs provide you and your loved ones with wellness support for key areas in your life:
Financial wellness
We're committed to enabling your financial success and provide competitive offers and programs.
Physical wellness
We're committed to providing benefits that support your physical health and wellness.
Personal wellness
We're committed to providing resources that help support the full scope of your life.
How we work
Vanguard has implemented a hybrid working model for most of our employees (crew members), designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.
How we rate this
Data Scientist, Specialist at Vanguard rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- How do you think about the risk of an AI system in this kind of role failing silently?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: RAG, AI Agents, NLP, and AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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