Director Data Science

ABOUT CREDGENICS

• Credgenics is Indias leading SaaS platform for debt collections and resolution, working with banks, NBFCs, ARCs, and
• fintech lenders. Our platform spans digital communications, field collections, legal workflows, payments, and AI-driven
• collections strategy — now expanding into multilingual AI voice bots for large-scale collection conversations.

THE ROLE

We are hiring a Head of Data Science to own two mandates:

(1) Applied AI for our Voice Bot — the single most important product bet at Credgenics today, and (2) the core Data Science charter powering collections strategy across the platform.

This is a leadership role — you will set strategy, build the team, and drive technical direction. You must be technically deep enough to review evals, challenge model choices, and make build-vs-buy decisions with conviction.

WHAT YOU WILL OWN

Voice AI — Flagship Mandate

• • Own end-to-end ML quality of the voice bot pipeline: STT accuracy on noisy Indian-language telephonic audio,
• TTS naturalness, LLM conversation quality, and full-loop latency (VAD, endpointing, streaming)
• • Build rigorous offline and online eval frameworks — disposition accuracy, script & regulatory compliance,
• hallucination/safety red-teaming, containment, and conversion (PTPs, payments). Every model change ships
• against an eval
• • Make vendor and architecture decisions: STT/TTS/LLM selection, fine-tuning vs. prompting, self-hosted vs. API,
• and data residency for Indian BFSI clients
• • Build the data flywheel: turn call recordings, transcripts, dispositions, and QC audits into training data and
• continuous improvement loops
• • Partner with Product and Engineering on conversation design — objection handling, drop-off analysis, call-
• outcome modelling, and per-client tuning
• • Building and training in-house STT and TTS models in the longer run.

Collections Data Science

• • Own and improve the predictive model suite: propensity to pay, best time/channel to contact, agency allocation,
• settlement & legal-action propensity, roll-rate and recovery forecasting
• • Build an experimentation culture: champion/challenger testing, A/B frameworks, and uplift measurement that
• clients trust

• Package model outputs and portfolio insights as product features, not one-off decks
• Own model monitoring, drift detection, retraining pipelines, and audit-ready documentation

Leadership & Governance

• Hire, grow, and retain a high-performing team of data scientists, ML engineers, and applied AI engineers
• Set the DS/AI roadmap jointly with Product and Engineering; present outcomes and trade-offs to founders and client CXOs
• Own model governance: explainability, bias/fairness checks, auditability, and compliance with RB expectations and Indias DPDP Act
• Establish MLOps foundations: experiment tracking, model registry, versioning, monitoring, and rollback

WHAT WE ARE LOOKING FOR

Must Have

• 10+ years in data science / applied ML, with 4+ years leading and scaling teams
• Demonstrated production experience with LLM-based or speech systems in the last 2 years — you have shipped conversational AI, GenAI apps, or speech pipelines at real scale
• Strong classical ML foundations: supervised learning, scorecards/propensity models, experimentation, and causal measurement
• Experience deploying models in regulated environments (BFSI strongly preferred; lending/collections a major plus)
• Ability to make architecture and vendor decisions under commercial constraints — you think in unit economics, not just accuracy metrics
• Clear communication with engineers and business leaders; comfortable presenting to clients.

Strong Plus

• Hands-on exposure to the modern voice stack: STT (Deepgram, Sarvam, Whisper), TTS (ElevenLabs, Azure), real-time orchestration (Pipecat, LiveKit), telephony integration
• Multilingual and vernacular NLP experience — code-switched conversations, regional accents, and ASR/TTS quality on Indian-language telephonic audio
• Experience with LLM eval tooling, red-teaming, and guardrail design
• Prior experience in lending, collections, or contact-centre AI

WHAT SUCCESS LOOKS LIKE (FIRST 12 MONTHS)

• Voice bot quality metrics (containment, disposition accuracy, compliance, PTP conversion) are defined, instrumented, and improving MoM with a rigorous eval pipeline behind every release.
• Vendor stack decisions (STT/TTS/LLM, hosting) are settled with clear cost and quality rationale
• Core collections models are refreshed, monitored, and demonstrably lifting client recovery metrics
• A strong DS/Applied AI team is in place with clear ownership and low regrettable attrition
• Banking clients trust our AI: governance documentation, explainability, and audit readiness are in place