Job Description

Leader - Data Science

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Incedo Data Modernization Platforms | DataXel & DQXpert

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LOCATION - Gurugram/Bangalore

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EXPERIENCE - 14–18 years

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REPORTS TO Head of Engineering

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ABOUT INCEDO

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Incedo is a global AI and data transformation specialist, helping companies turn digital investment into sustainable business impact by delivering ROI from AI@Scale . We are 4,000+ people across the US, Canada, Latin America and India, working with Fortune 500 enterprises and rapid-growing clients in banking & payments, wealth management, telecom, hi-tech and life sciences.

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Build what's next in AI, data and enterprise platforms

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Our Platform & Solutions portfolio is where Incedo builds AI-native products for real enterprise problems: Incedo Lighthouse (AI-powered decision intelligence), DataXel (agentic data modernization), brAInspark (agentic AI enablement), IncedoPay (integrated payables), Kratos (regulatory compliance and data control for banking) and DQXpert (AI-powered data quality) — plus domain products across customer support, document processing, quality engineering, healthcare and financial services.

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You'll work with multidisciplinary teams across Product, Engineering, AI/Data, Design and Business, taking ideas from problem discovery and experimentation to production-grade platforms that enterprises depend on.

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WHY THIS ROLE

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Most Director of Data Science roles sit in one camp: a pure analytics function, or a GenAI product team. This one needs both, working together.

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DataXel needs a data science Leader who can design autonomous agents that take consequential actions on live enterprise data estates — safely. DQXpert needs someone who can make generative AI trustworthy enough that business users will actually act on what it tells them about their data.

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THE ROLE

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You will be the Data Science Leader for DataXel and DQXpert , Incedo’s data modernization product suite. You set the modelling and agentic architecture strategy, lead the data science bench, and own the intelligence layer of both products from prototype to production.

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Most Director of Data Science roles sit either inside a pure analytics function or on top of a GenAI product. This one needs both. DataXel is not a model — it is an autonomous agent taking consequential actions on live enterprise data estates, so it needs someone fluent in multi-step planning, tool orchestration and safe autonomous execution.



DQXpert needs generative AI trustworthy enough that business users act on what it tells them — which takes classical ML rigour to build the deterministic guardrails underneath.

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WHAT YOU'LL OWN

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1. The agentic modernization engine — DataXel

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- Lead the design and evolution of DataXel's agentic architecture: autonomous schema discovery, intelligent mapping, pipeline generation and migration execution, orchestrated across a multi-step agentic loop.
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- Define the human-in-the-loop gate framework — where approval is required, how confidence and uncertainty are surfaced to the reviewer, and how the system escalates gracefully when thresholds aren't met.
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- Build the learning system that turns every HITL approval and rejection into a feedback signal that improves future automation.
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- Partner with data engineering to ensure the agentic execution layer integrates safely with DataXel's transformation engine.
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2. GenAI-powered data quality — DQXpert

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- Lead the design of DQXpert's GenAI layer: natural-language data profiling, LLM-based anomaly characterisation, and plain-language DQ rule authoring that turns business intent into executable validation logic.
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- Architect the knowledge context layer behind remediation recommendations — how domain knowledge, past fixes and lineage context get retrieved and injected into prompts to produce specific, trustworthy suggestions instead of generic ones.
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- Build the continuous improvement loop: track accept/reject behaviour on rules and recommendations, and use it to sharpen what the system surfaces.
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- Own the classical ML and statistical models where deterministic correctness beats generative fluency — anomaly detection baselines, schema drift scoring, and the DQ metrics the GenAI layer reasons on top of.
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3. Platform architecture & MLOps

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- Define the AI architecture across the suite: model versioning, agentic workflow orchestration, RAG pipeline management, feedback instrumentation and production monitoring.
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- Ensure every autonomous action in DataXel and every generated output in DQXpert has defined confidence thresholds,



fallback behaviour and an audit trail — so both products fail safely and visibly.
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- Champion responsible AI: hallucination mitigation, PII-safe prompting, compliance-conscious design, and explainability that end users can follow.
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4. Team leadership, Client Enagement & Presales

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- Mentoring and growing the data science bench, partnering with Engineering and Product on roadmap, and representing both platforms credibly in client conversations, POCs and pre-sales.
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WHAT YOU'LL BRING

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- 14–18 years in data science and applied AI, including 3+ years leading technically at Director level or equivalent — and still hands-on.
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- Deep classical ML: anomaly detection, time-series, schema matching, entity resolution, clustering, classification — enough depth to build the guardrails that make generative output trustworthy.
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- Production LLM/GenAI engineering: prompt design, RAG architectures, fine-tuning, knowledge-context design.
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- Hands-on agentic platform design: multi-step LLM orchestration, tool use, autonomous execution and HITL patterns — LangGraph, LangChain, CrewAI or equivalent.
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- Feedback-loop systems: RLHF-adjacent methods, behavioural signal capture, continuous improvement from human interaction data.
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- Production-grade Python (pandas, scikit-learn, PySpark), plus working fluency with data pipeline and ETL ecosystems (Informatica, dbt, Spark, AWS Glue or equivalent).
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- Cloud data platforms: AWS (S3, Redshift, Glue, EMR) or Azure/GCP equivalent, and lakehouse architectures.
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- MLOps/LLMOps in production: model registries, automated retraining, drift detection, RAG pipeline monitoring.
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- Responsible AI instincts: hallucination mitigation, PII-safe prompting, explainability and audit-ready design.
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Nice to have

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- Built data quality, observability or data profiling intelligence — in a product company or at serious scale in client delivery.
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- Familiarity with data mesh or data fabric patterns and what they mean for distributed data quality enforcement.
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- AWS certifications (Machine Learning Specialty, Data Analytics Specialty or equivalent).
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- Consulting or SI background with exposure to enterprise-scale data transformation programmes.
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Education

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- B.Tech / M.Tech / M.S. in Computer Science, Statistics, Mathematics or a closely related technical discipline. Equivalent industry experience considered.
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Interested candidates can share their resume at [email protected] !!

📌 Director Data Science (Bengaluru)
🏢 Incedo
📍 Bengaluru