Senior Data Scientist

Title: Senior Data Scientist

Location: Must be onsite in McLean, VA for 5 days a week (Monday to Friday)

Duration: Long Term

Interview Mode: In-Person interview

Notes:

Looking for a Senior Data Scientist with strong Python and Computer Vision skills to design, build, test, and operationalize capabilities that convert image-based content and model outputs into usable, reviewable, and analytics-ready data products

Need someone with hands-on experience coding computer vision or image-processing solutions using Python and common libraries such as OpenCV, Pillow, PyTorch, TensorFlow, or similar frameworks.

Need someone with strong ability to process image files, extracted labels, model predictions, confidence scores, annotations, bounding boxes, and metadata payloads

Need someone who has experience writing modular, maintainable code for automation, validation, transformation, testing, and troubleshooting

Job Description:
We are seeking a hands-on Data Scientist with strong Python and Computer Vision skills to design, build, test, and operationalize capabilities that convert image-based content and model outputs into usable, reviewable, and analytics-ready data products. This role requires strong software engineering fundamentals, computer vision coding experience, data engineering skills, and the ability to partner across product, modeling, engineering, research, and business teams.

The Data Scientist will contribute to capabilities for image extraction, metadata generation, model-output validation, quality review enablement, and downstream structured data integration. The role is expected to balance Python development, testing, analytical troubleshooting, and delivery execution to help users review, validate, and act on computer vision outputs.

Key Responsibilities:

Computer Vision Development & Model Output Engineering

Develop, enhance, and maintain code that supports computer vision model output processing, image extraction, metadata generation, and validation workflows

Work with image-based model outputs, bounding boxes, labels, confidence scores, extracted attributes, and structured metadata to support downstream review and analysis

Build reusable utilities for parsing, transforming, validating, and comparing computer vision outputs across model versions and production-style runs

Apply strong Python coding practices to automate testing, issue detection, data preparation, and model-output quality checks

Product & QC Workflow Enablement

Support product capabilities that allow users to review, validate, correct, and quality check model-generated outputs

Translate computer vision models output into user-facing review patterns, QC screens, exception workflows, and validation experiences

Partner with UI developers, product owners, and business users to define practical capabilities for model-output inspection and operational review

Test Data, Validation & Quality Engineering

Create and manage representative test datasets for image extraction, metadata validation, regression testing, and model performance review

Perform structured testing of model runs across historical and current datasets to identify extraction gaps, metadata issues, formatting errors, and quality concerns

Validate extracted images, image classifications, and metadata against original PDFs, appraisal reports, and other authoritative source documents to confirm completeness, accuracy, and traceability

Document defects with clear evidence, expected results, actual results, severity, reproducible examples, and recommended remediation steps

Retest remediated issues and contribute to repeatable quality gates for model-output readiness

Data Integration, JSON Engineering & Analytics

Readiness Develop scripts and data pipelines that convert model outputs into structured and semi-structured formats suitable for research, analytics, and downstream consumption

Support loading and validation of model outputs as JSON Variant or similar semi-structured data formats

Ensure extracted image attributes, metadata, and model-output payloads are traceable, consistent, and accessible for analysis

Cross-Functional Delivery & Technical Problem Solving

Collaborate across product management, model development, UI engineering, data engineering, research, business, and delivery teams to operationalize computer vision capabilities within data-driven products

Investigate technical issues across image inputs, model outputs, metadata payloads, data loads, and user-facing QC workflows

Communicate progress, risks, blockers, and technical findings clearly to engineering partners and business stakeholders

Required Qualifications:

Computer Vision Coding & Software Engineering

Hands-on experience coding computer vision or image-processing solutions using Python and common libraries such as OpenCV, Pillow, PyTorch, TensorFlow, or similar frameworks

Strong ability to process image files, extracted labels, model predictions, confidence scores, annotations, bounding boxes, and metadata payloads

Experience writing modular, maintainable code for automation, validation, transformation, testing, and troubleshooting

Data Engineering & Semi-Structured Data

Strong SQL and Python skills for working with relational data, semi-structured data, JSON, API outputs, and analytical datasets

Experience preparing model outputs for downstream systems using JSON, Variant-style data structures, metadata files, or similar formats

Ability to design validation logic, reconciliation checks, and data quality rules for image-derived outputs

Testing, Debugging & Quality Validation

Experience testing model-output pipelines, identifying defects, analyzing root causes, documenting issues, and supporting retesting after remediation

Ability to create representative test datasets and compare expected versus actual computer vision output across runs

Experience validating extracted image outputs against source PDFs, appraisal documents, supporting files, and other ground-truth reference materials

Strong analytical skills to detect anomalies, data gaps, misclassifications, format issues, and model-output inconsistencies

Product, UI & Workflow Collaboration

Experience working with product and engineering teams to support user-facing applications, QC workflows, review screens, or operational tools

Ability to translate technical model-output structures into practical user, data, and system requirements

Strong collaboration, communication, ownership, and delivery execution skills in a fast-paced technical environment

Preferred Qualifications:

Experience with appraisal images, property photos, mortgage data, or other document/image-heavy business processes

Familiarity with image extraction, object detection, classification, OCR, metadata extraction, or computer vision evaluation techniques

Experience comparing extracted image content and metadata back to source documents to support auditability, traceability, and quality control

Exposure to Snowflake Variant, JSON analytics pipelines, data lake patterns, or research data environments

Experience supporting Freddie Mac, Fannie Mae, GSE, financial services, housing, or appraisal-related technology initiatives

Familiarity with UAD, appraisal modernization, model validation, or AI-enabled quality control workflows.