Director Data Science

About Incedo

Incedo is a global leader in AI and data transformation, enabling businesses to achieve measurable returns on digital investments through AI at scale. With over 4,000 employees operating across the US, Canada, Latin America, and India, Incedo serves leading enterprises and dynamic clients in sectors like banking, wealth management, telecom, hi-tech, and life sciences.

Role Overview

The Director of Data Science will lead initiatives for Incedo’s DataXel and DQXpert products—key components of the company’s data modernization platform. This position requires blending advanced analytics expertise with generative AI product leadership to advance autonomous agent-based data operations and trustworthy generative AI for data quality management.

Key Responsibilities

• Lead DataXel’s agentic modernization engine architecture, enabling autonomous schema discovery, intelligent mapping, pipeline creation, and migration managed via multi-step agentic loops.
• Design the human-in-the-loop (HITL) framework to manage approval gates, display confidence metrics, and implement escalation mechanisms for uncertain outcomes.
• Develop learning systems that convert human feedback from HITL approvals or rejections into signals that improve process automation.
• Collaborate with data engineering to ensure seamless and safe integration of execution layers with DataXel’s transformation engine.
• Architect DQXpert's generative AI components including natural language data profiling, LLM-driven anomaly characterization, and user-friendly data quality rule authoring that translates business intent to executable validations.
• Develop a knowledge context framework for remediation suggestions that incorporates domain knowledge, historical fixes, and data lineage ensuring accurate and trustworthy AI-driven advice.
• Implement continuous improvement loops based on acceptance or rejection of rules and suggestions to refine system outputs.
• Maintain classical machine learning models targeting anomaly detection, schema drift assessment, and data quality metrics that support generative AI layers.
• Define AI architecture across products, covering model versioning, agentic workflow orchestration, retrieval-augmented generation pipelines, feedback instrumentation, and production monitoring.
• Ensure all autonomous actions and generated outputs have confidence thresholds, fallback protocols, and audit trails for safe failure modes.
• Champion responsible AI principles such as hallucination mitigation, privacy-safe prompting, compliance-aware designs, and clear explainability for end-users.
• Provide leadership and mentorship for the data science team, collaborate with engineering and product teams on roadmaps, and engage effectively with clients during presentations, proof of concepts, and pre-sales discussions.

Candidate Profile

• 14 to 18 years of professional experience in data science and applied AI, including at least 3 years in a senior technical leadership role (Director level or equivalent) with continued hands-on involvement.
• Strong expertise in classical machine learning techniques such as anomaly detection, time-series analysis, schema matching, entity resolution, clustering, and classification, essential for building trustworthy guardrails below generative outputs.
• Proven ability in production-level Large Language Model and generative AI engineering, including prompt engineering, retrieval-augmented generation architecture design, fine-tuning, and knowledge context systems.
• Experience designing agentic platforms incorporating multi-step LLM orchestration, tool integration, autonomous execution workflows, and human-in-the-loop feedback (technologies like LangGraph, LangChain, CrewAI or similar).
• Capability in feedback-loop systems involving reinforcement learning with human feedback adjacent methods, behavior signal capture, and continuous learning from user interactions.
• Proficiency in Python programming for production environment using pandas, scikit-learn, and PySpark, combined with practical knowledge of data pipeline and ETL tools such as Informatica, dbt, Spark, AWS Glue, or equivalents.
• Hands-on experience with cloud data platforms including AWS services like S3, Redshift, Glue, EMR or equivalent platforms on Azure/GCP, and familiarity with lakehouse data architectures.
• Expertise in MLOps and LLMOps best practices for production deployments, encompassing model registries, automated retraining, drift detection, and pipeline monitoring.
• Strong commitment to responsible AI, including techniques for hallucination mitigation, privacy-safe prompting, explainability, and audit-ready implementations.

Preferred Qualifications

• Prior involvement in building data quality, observability or data profiling intelligence solutions, either as part of product development or at scale in client projects.
• Knowledge of data mesh and data fabric architectural patterns and their implications for enforcing distributed data quality.
• Relevant AWS certifications such as Machine Learning Specialty or Data Analytics Specialty.
• Experience in consulting or systems integration focusing on enterprise-scale data transformation projects.

Education

A bachelor’s or master’s degree in Computer Science, Statistics, Mathematics, or closely related technical fields is required. Equivalent industry experience is also considered.