Data Scientist with Engineer

Data Scientist with EngineerLocation: Onsite Mclean VA, 5 days a week in (Locals Only)Notes:Need Exp. in Python and Modeling, supporting computer visionThis candidate will extract image information and inputs and extract attributes in metadataNeed Exp with Data engineering, python (Coding), SQL, Snowflake and AI for Computer Vision model is requiredImage processing models required like - Open CV, TensorFlow, PyTorchExp with Data Extraction from Images is requiredProblem solving skills requiredMortgage and Financial exp is Strongly PreferredNot a primarily data Scientist role bit of Data engineering requiredValidate output and data from ExtractionThis Resource will be investigating issues and working with data engineering team and Modeling team to find solutions.Clear Technical team Interview for - 1 Hour – 1 round only.OverviewWe are seeking a hands-on Computer Vision Developer to support the IRIS product by designing, building, testing, and operationalizing capabilities that convert appraisal image 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, UI, research, and business teams.The developer will contribute to product capabilities for image extraction, metadata generation, model-output validation, QC workflow enablement, and downstream JSON-based data integration. The role is expected to balance coding, testing, analytical troubleshooting, and delivery execution that enables IRIS users to review and act on computer vision outputs.Key Responsibilities· Computer Vision Development & Model Output Engineeringo Develop, enhance, and maintain code that supports computer vision model output processing, image extraction, metadata generation, and validation workflowso Work with image-based model outputs, bounding boxes, labels, confidence scores, extracted attributes, and structured metadata to support downstream review and analysiso Build reusable utilities for parsing, transforming, validating, and comparing computer vision outputs across model versions and production-style runso Apply strong Python coding practices to automate testing, issue detection, data preparation, and model-output quality checks· IRIS Product & QC Workflow Enablemento Support IRIS product capabilities that allow users to review, validate, correct, and quality check model-generated outputso Translate computer vision model outputs into user-facing review patterns, QC screens, exception workflows, and validation experienceso Partner with UI developers, product owners, and business users to define practical capabilities for model-output inspection and operational review· Test Data, Validation & Quality Engineeringo Create and manage representative test datasets for image extraction, metadata validation, regression testing, and model performance reviewo Perform structured testing of model runs across historical and current datasets to identify extraction gaps, metadata issues, formatting errors, and quality concernso Validate extracted images, image classifications, and metadata against original PDFs, appraisal reports, and other authoritative source documents to confirm completeness, accuracy, and traceabilityo Document defects with clear evidence, expected results, actual results, severity, reproducible examples, and recommended remediation stepso Retest remediated issues and contribute to repeatable quality gates for model-output readiness· Data Integration, JSON Engineering & Analytics Readinesso Develop scripts and data pipelines that convert model outputs into structured and semi-structured formats suitable for research, analytics, and downstream consumptiono Support loading and validation of IRIS model outputs as JSON Variant or similar semi-structured data formatso Ensure extracted image attributes, metadata, and model-output payloads are traceable, consistent, and accessible for analysis· Cross-Functional Delivery & Technical Problem Solvingo Collaborate across product management, model development, UI engineering, data engineering, research, business, and delivery teams to operationalize computer vision capabilities within the IRIS producto Investigate technical issues across image inputs, model outputs, metadata payloads, data loads, and user-facing QC workflowso Communicate progress, risks, blockers, and technical findings clearly to engineering partners and business stakeholdersRequired Qualifications:· Computer Vision Coding & Software Engineeringo Hands-on experience coding computer vision or image-processing solutions using Python and common libraries such as OpenCV, Pillow, PyTorch, TensorFlow, or similar frameworkso Strong ability to process image files, extracted labels, model predictions, confidence scores, annotations, bounding boxes, and metadata payloadso Experience writing modular, maintainable code for automation, validation, transformation, testing, and troubleshooting· Data Engineering & Semi-Structured Datao Strong SQL and Python skills for working with relational data, semi-structured data, JSON, API outputs, and analytical datasetso Experience preparing model outputs for downstream systems using JSON, Variant-style data structures, metadata files, or similar formatso Ability to design validation logic, reconciliation checks, and data quality rules for image-derived outputs· Testing, Debugging & Quality Validationo Experience testing model-output pipelines, identifying defects, analyzing root causes, documenting issues, and supporting retesting after remediationo Ability to create representative test datasets and compare expected versus actual computer vision output across runso Experience validating extracted image outputs against source PDFs, appraisal documents, supporting files, and other ground-truth reference materialso Strong analytical skills to detect anomalies, data gaps, misclassifications, format issues, and model-output inconsistencies· Product, UI & Workflow Collaborationo Experience working with product and engineering teams to support user-facing applications, QC workflows, review screens, or operational toolso Ability to translate technical model-output structures into practical user, data, and system requirementso Strong collaboration, communication, ownership, and delivery execution skills in a fast-paced technical environmentPreferred 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