Senior Data Scientist

Responsibilities

• Develop end-to-end Generative AI solutions leveraging LLMs and foundational AI components
• Design and implement scalable RAG (Retrieval-Augmented Generation) workflows
• Develop complex queries and performs extensive programming to access, transform, and prepare data for statistical modeling.
• Leads and executes deep dive diagnostic, predictive, and prescriptive analytics to support data-driven business decision making.
• Mentors and develops junior data scientists and analysts.
• Identifies and diagnoses data inconsistencies and errors, documents data assumptions, and forages to fill data gaps.
• Engages with internal stakeholders to understand and probe business processes in order to develop hypotheses.
• Brings structure to requests and translates requirements into an analytic approach.
• Guides test design, research design, and model validation.
• Provides statistical consultation services.
• Serves as the analytics expert on cross functional teams for large strategic initiatives and contributes to the growth of the Vanguard analytic community.
• Prepares and delivers insight presentations and action recommendations.
• Communicates complex analytical findings and implications to business leaders.
• Participates in special projects and performs other duties as assigned.
• Designs scalable data mining models and reporting protocols to identify trends and patterns in massive datasets.
• Applies modern and emerging data science techniques to solve business problems across functions such as marketing, economics, and operations.
• Contributes to the advancement of analytics through exploratory research and application of cutting-edge statistical theory.

• 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
• Expertise in programming with Python, R, or SQL; hands‑on experience with machine learning libraries (e.g., Scikit-learn, Tensor Flow); strong understanding of data pipelines, data wrangling, and statistical techniques; experience in big data environments (Spark/Hadoop); familiarity with visualization tools like Tableau or Power BI