Role Summary

We are looking for a hands-on Data Scientist with a robust statistics and AI foundation to work across raw, unstructured, and behavioral data engineering features, building advanced models, and shipping production-grade deliverables in close collaboration with the client's data and engineering teams.

Key Responsibilities

Mine raw credit bureau (tradeline-level) and bank transaction data to identify, structure, and validate discrete, machine-readable risk signals with clear trigger conditions and supporting evidence.

Apply LLM-based extraction techniques to convert unstructured data (e.g., sales call transcripts) into structured, schema-validated signals (claim credibility, objection type/resolution, sentiment) suitable for downstream models not narrative summaries. Engineer features from high-frequency, noisy behavioral/interaction event data using sequence and trajectory analysis, clustering, and state-based modeling. Build and validate advanced models spanning classical ML, survival/hazard modeling (Cox, parametric, ML survival models), and Bayesian methods (hierarchical models, empirical Bayes, sequential updating). Rigorously test whether new signals/features add incremental predictive value beyond existing structured models avoid replicating what current models already capture. Take models and features from experimentation through to production-grade deliverables: version-controlled code, reproducible pipelines, and monitored production services.



Own Python codebases with GitHub-based version control and CI/CD workflows for feature/model deployment. Contribute reusable content, frameworks, and learnings to our internal AI Center of Excellence (CoE). Operate entirely within access-controlled infrastructure, working only with de-identified data under named-user access.

EXPERTISE AND QUALIFICATIONS

Required Skills & Experience

Strong foundation in statistics and AI, including familiarity with the latest AI tools and frameworks (LLM-based extraction, GenAI/agentic workflows). Hands-on data science experience with feature engineering from raw/unstructured data (not just pre-aggregated variables). Advanced modelling capability: classical ML (XGBoost, Random Forest, etc.), sequence/trajectory methods (e.g., PrefixSpan, HDBSCAN, HMMs), survival/hazard modelling, and Bayesian statistical modelling. Demonstrated ability to move analytical work from insight to production-grade deliverable (APIs, libraries, or service wrappers not just notebooks). Proficiency in Python, with working experience in Git-based version control and CI/CD pipelines. Comfort working in regulated, sensitive-data environments with strict data handling and no-retention constraints. Strong written and verbal communication skills for a distributed offshore/onsite team model.

Nice to Have

Prior experience in financial services / lending, credit bureau, or transaction-screening data. Experience building agentic evaluation/observability frameworks for AI systems (drift detection, hallucination checks)

📌 Principal Data Scientist (Bengaluru)
🏢 Crescendo Global Leadership Hiring India
📍 Bengaluru