Senior Data Scientist

Role & responsibilities

Senior Data Scientist

We 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 design

Data 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 workloads

Governance & 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 environments

Experience

• 12+ 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 models

Preferred 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