Data Scientist (Classical ML/NLP + LLM)

Job Summary

We are looking for a Data Scientist to lead high impact Data Science initiatives that convert diverse data into actionable insights and decision support. You will be responsible for designing, executing and implementing solutions that drive research excellence, operational efficiency and revenue growth.

Responsibilities

• Partner with stakeholders to frame the problem. Work directly with technical leaders and domain experts to translate ambiguous questions into well-posed analytical problems, align on success metrics, communicate trade-offs clearly, and recommend the most effective approach-ML/AI, classical analytics, or rule-based decisioning-based on impact, constraints, and maintainability
• Develop methodologies across the full spectrum. Apply and extend methods across supervised learning, unsupervised learning, Generative / Agentic AI, NLP, etc
• Apply LLMs and agentic systems pragmatically, and drive adoption of evolving capabilities. Continuously evaluate new models, tooling, and patterns; run fast, measurable prototypes; and scale the winners into production RAG/agent workflows-owning evaluation, guardrails, and hallucination control
• Deliver production ready solutions. Derive approaches from first principles when off-the-shelf methods don t fit, prototype hands-on, and carry solutions from data cleaning and feature engineering through model development, evaluation, deployment-ensuring quality in terms of its accuracy, stability and scalability
• Communicate insights in business terms. Convey complex technical concepts to diverse audiences and influence key stakeholders and leaders across the organization

Requirements

• 3- 6 years in applied ML / applied AI, with hands-on experience building and deploying models in production environments
• Bachelors or Masters (or PhD) in Computer Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field. Equivalent demonstrable expertise is welcome
• Demonstrated experience working across multiple areas such as supervised ML, unsupervised ML, optimization, recommendation systems, forecasting, and LLM/agentic systems/ RAG, agentic workflows, evaluation frameworks, prompt engineering, fine-tuning trade-offs, and vector databases
• Proven track record of owning solutions end-to-end-from problem framing and method selection to productionization, monitoring, and iteration in real-world environments
• Experience with graph algorithms is a plus (knowledge graphs, graph traversal, community detection, graph rag etc
• Experience in business applications such as churn analysis, customer profiling, and recommendation systems
• NLP techniques: vector databases, embeddings, topic modeling, and fine-tuning of transformer models like BERT to enhance text analytics capabilities
• Expert-level Python. Comfortable with the scientific stack (NumPy, pandas, scikit-learn, PyTorch) and with writing clean, tested, modular code
• Distributed computing (Spark / Snowflake / BigQuery) for large-scale data and training
• Cloud (AWS / Azure / GCP) - model training, deployment, scaling, cost-awareness
• Working knowledge of Agentic AI Frameworks like LangChain, LangGraph and Deep-Agents or equivalent
• Deep learning fundamentals: understanding when to apply deep learning approaches and how to train and evaluate them effectively in practice

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