Data Scientist (Classical ML, NLP & LLM/GenAI/Agentic AI)
Hybrid · Gurugram, Haryana, India
Job Summary
Data Scientist to lead high-impact data science initiatives that convert diverse data into actionable insights and decision support. You will design, execute and implement solutions that drive research excellence, operational efficiency and revenue growth. Responsibilities include framing problems with stakeholders, translating ambiguous questions into well-posed analytical problems, selecting appropriate ML/AI approaches (including classical analytics, ML, or rule-based decisioning), developing methodologies across supervised/unsupervised learning, Generative/Agentic AI and NLP, applying and evaluating LLMs and agentic systems, delivering production-ready solutions from data cleaning and feature engineering to model deployment, and communicating insights in business terms to influence leadership. Requirements cover 3-6 years of applied ML/AI experience, strong programming with Python, experience with production deployments, and familiarity with tools and frameworks for modern AI workflows; plus experience with cloud platforms and agentic AI toolkits. The employer emphasizes a hybrid work environment and a collaborative culture, with a focus on solving real-scale problems and delivering measurable impact at Gartner.
Required Qualifications
- 3-6 years in applied ML / applied AI
- Bachelor's or Master's (or PhD) in Computer Science, Statistics, Mathematics, Engineering, Economics, or related quantitative field
- Experience across supervised ML, unsupervised ML, optimization, recommendation systems, forecasting, and LLM/agentic systems/RAG
- Prompt engineering, fine-tuning trade-offs, vector databases
- Expert-level Python (NumPy, pandas, scikit-learn, PyTorch)
- Distributed computing (Spark / Snowflake / BigQuery) for large-scale data and training
- Cloud (AWS / Azure / GCP) — model training, deployment, scaling, cost-awareness
- Knowledge of Agentic AI Frameworks like LangChain, LangGraph and Deep-Agents or equivalent
- NLP techniques: embeddings, topic modeling, transformer fine-tuning (e.g., BERT)
- Experience delivering production-ready solutions from data cleaning to deployment
- Ability to communicate insights to non-technical stakeholders
- Knowledge graphs or graph algorithms is a plus
- Experience in business applications like churn analysis, customer profiling, and recommendation systems
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