Overview

Data Scientist (AI & ML)

Responsibilities

• Translate business requirements into well-defined analytical problems with a clear decision, baseline and evaluation metric
• Select the appropriate approach (rules-based, classical machine learning, deep learning, a fine-tuned model or an LLM) based on accuracy, cost, latency and explainability, and document the rationale
• Assess data feasibility, including label quality, leakage and bias, and advise where the data is insufficient for the objective
• Develop and validate models for classification, regression, ranking and forecasting, starting from robust baselines
• Develop fraud and anomaly detection models for imbalanced settings with limited labelled data
• Generate labels through weak supervision, active learning and LLM-assisted labelling, with human validation
• Develop LLM-based solutions, including retrieval-augmented generation, document extraction, summarisation and tool-using agents
• Design retrieval components (chunking, embeddings, hybrid search and re-ranking), and adapt models through prompting, parameter-efficient fine-tuning and distillation
• Develop deep learning solutions for NLP, document understanding (OCR and vision-language models), vision and speech, including Indian-language and code-mixed data
• Optimise cost and latency, including the choice between open-weight and API-based models
• Validate models using leakage-free schemes, and quantify uncertainty through prediction intervals, calibration and conformal prediction
• Evaluate generative AI systems through golden datasets, separate retrieval and generation metrics, hallucination checks, and LLM-based evaluation calibrated against human labels
• Conduct error and subgroup analysis across cohorts, languages and input types
• Design experiments and apply quasi-experimental methods to assess whether deployed models have improved outcomes
• Develop tested, peer-reviewed, production-quality Python code and ensure all experiments are reproducible
• Hand over models to the MLOps team with defined monitoring metrics, drift thresholds and retraining triggers
• Monitor deployed systems for quality, drift, latency and cost, and incorporate failures into evaluation sets
• Measure and mitigate performance disparities across cohorts, and provide explanations for decisions affecting individuals
• Test LLM systems for prompt injection, jailbreaks and leakage of personal data, and design guardrails in coordination with security specialists
• Apply anonymisation and data minimisation in compliance with applicable data protection law, and prepare model cards and evaluation records
• Prepare clear reports for non-technical stakeholders, including model limitations and uncertainty
• Review the work of junior data scientists, and support the review of vendor model deliverables
• Statistics & Causal Inference: generalised linear and hierarchical models, Bayesian inference, hypothesis testing, bootstrap methods; A/B testing, difference-in-differences, synthetic control, regression discontinuity (DoWhy, EconML)
• Machine Learning: XGBoost, LightGBM and CatBoost; tabular foundation models (TabPFN); anomaly detection, clustering and graph methods; forecasting with statistical models and time-series foundation models (Chronos-2, TimesFM)
• Generative AI & LLMs: prompt design, structured outputs and tool calling; retrieval-augmented generation with hybrid search and re-ranking; vector databases (pgvector, Qdrant, Milvus or Weaviate) and FAISS; open-weight and API-based models; agent frameworks (LangGraph, LlamaIndex or equivalent) and the Model Context Protocol (MCP)
• Model Adaptation & Serving: LoRA or QLoRA fine-tuning and preference optimisation (DPO, GRPO) using Hugging Face TRL, PEFT or Unsloth; distillation; quantisation; efficient serving (vLLM or SGLang)
• Deep Learning & Document AI: PyTorch and Hugging Face Transformers; vision-language models for document parsing (such as PaddleOCR-VL, Docling or the Qwen-VL family); vision models for classification and detection; speech recognition (IndicConformer, Whisper or newer open models)
• Indic Language AI: AI4Bharat models (IndicConformer speech recognition for 22 Indian languages, IndicTrans2 translation, IndicBERT, Indic Parler-TTS), Sarvam models and Bhashini services; handling of code-mixed and transliterated text
• Evaluation & Calibration: RAGAS, DeepEval, promptfoo or Inspect; retrieval metrics (recall at k, MRR, nDCG); calibration and conformal prediction; SHAP and Fairlearn
• Data, Engineering & Observability: advanced SQL, PySpark, Polars or DuckDB; Python with pytest, Git, MLflow, DVC and Docker; LLM tracing and observability (Langfuse or Arize Phoenix, OpenTelemetry)

Qualifications

• 4+ years in data science, machine learning or applied AI (6+ years preferred); minimum 3 years developing models or AI systems deployed in production or used in operational decisions
• Demonstrable end-to-end ownership of at least two models or AI systems, with measured outcomes
• Hands-on experience with at least one LLM or deep learning application deployed for end users, with documented evaluation
• Experience in at least two of the following: fraud or anomaly detection, forecasting, risk scoring, NLP and LLM applications, computer vision or document AI, speech, and causal inference
• Experience with large, heterogeneous datasets, including linkage of records across sources
• Experience in a regulated sector (financial services, healthcare, telecommunications or the public sector) or with Indian-language data is a plus).

Education

• B.Tech./B.E., M.Tech./M.S. or M.Sc. in Computer Science, Statistics, Mathematics, Data Science, AI/ML or a related quantitative discipline (Must have)
• Advanced degree (M.S./M.Tech./Ph.D.) or published applied work in machine learning, NLP, computer vision or statistics is highly desirable
• Certifications are not mandatory; candidates with strong verifiable work (Kaggle Expert level or above, published research or significant open-source contributions) will be considered in lieu of formal qualification