Senior Data Scientist - Machine Learning

Senior Data ScientistWe are looking for a Senior Data Scientist with deep expertise in machine learning, intelligent document processing, and cloud-native AI pipelines. You will lead the design and delivery of production-grade AI/ML components within a governed, human-in-the-loop automation platform, working across the full spectrum from raw data ingestion to model deployment and continuous improvement.Key Responsibilities :- Architect and implement multi-tier document extraction pipelines combining deterministic parsing, OCR, and large language model (LLM) inference, applying the right technique for each document category.- Design and train document classification models (e.g., using Amazon SageMaker) for routing documents to appropriate extraction strategies.- Develop and optimize prompt engineering patterns for LLM-based extraction (Amazon Bedrock), including confidence scoring, source-region anchoring, and hallucination detection strategies.- Build and maintain data validation frameworks: schema validation, cross-field consistency checks, and configurable confidence thresholds per field and document type.- Design canonical data models and normalization pipelines that consolidate heterogeneous source data into a unified schema with full field-level lineage.- Lead the design of agentic AI components (extraction orchestration, layout detection, exception analysis) with clearly defined capability envelopes, escalation logic, and human approval gates.- Define and track extraction accuracy metrics; drive continuous improvement loops using structured human feedback.- Collaborate with solution architects to ensure ML components integrate cleanly with serverless AWS infrastructure (Lambda, Step Functions, S3, Glue, DynamoDB, EventBridge).- Mentor junior team members and establish best practices for model governance, change management, and reproducibility.Machine Learning & AI :- Document classification using supervised ML (SageMaker or equivalent)- LLM prompt engineering for structured data extraction (Bedrock, OpenAI, or similar)- OCR post-processing : table reconstruction, merged cell handling, multi-line value normalization (Amazon Textract or equivalent)- Confidence scoring design and threshold calibration- Cosine similarity and document fingerprinting for template matching- Agentic AI orchestration patterns with governed fallback and escalation designData Engineering & Python :- Python : pandas, openpyxl, boto3 production-grade, not notebook-only- ETL pipeline design for structured (Excel/CSV), semi-structured (PDF tables), and unstructured document types- Schema design and canonical data modeling- Data quality frameworks : completeness detection, duplicate identification, cross-field validation- SQL and relational data modeling (PostgreSQL preferred)Cloud - AWS :- AWS Lambda, Step Functions, S3, SageMaker, Bedrock, Textract, Glue, DynamoDB, EventBridge, SQS, KMS- Serverless-first architecture patterns; cost-efficient compute design for seasonal/batch workloadsGovernance & MLOps :- Model registry, versioning, and change board processes- Audit trail design: immutable lineage from source document to output- CI/CD integration for ML pipeline components- RBAC and data security in multi-tenant cloud environmentsExperience :- 8+ years in data science or ML engineering roles- At least 2 production deployments involving intelligent document processing or NLP pipelines- Demonstrated experience designing human-in-the-loop systems, not just fully automated modelsPreferred Candidate Profile :- Power BI or Amazon QuickSight for operational dashboards- React or familiarity with audit workbench UI requirements for human-in-the-loop review queues (ref:hirist.tech)