Applied Computer Vision Data Scientist

1. Production Computer VisionHands-on experience building and evaluating models for at least one of the following:Object detectionImage classificationProduct or visual recognitionObject localizationVisual search or matchingMulti-camera perceptionImage-based inventory or location intelligenceCandidates should be able to explain the model architecture, dataset, evaluation methodology, release path, production failure modes, and measured outcome.2. Python and Modern ML FrameworksStrong Python development with hands-on experience using:PyTorchTensorFlowComparable production ML frameworksCandidates must be able to move beyond notebooks and contribute maintainable, tested code supporting model training, evaluation, data analysis, and production validation.3. Production Model Evaluation and Error AnalysisDeep understanding of:Precision and recallFalse positives and false negativesConfidence thresholdsCalibrationConfusion matricesError slicingSegment-level performanceGround-truth comparisonEnd-to-end business accuracyMonarch specifically requires candidates who can distinguish model failures from image-quality, annotation, camera, catalog, pipeline, post-processing, and location-conversion failures.4. Dataset Development and Annotation QualityHands-on ownership of:Dataset creation and curationTraining, validation, and test-set designAnnotation guidelinesLabel-quality auditsHard-example identificationDataset versioningDeduplicationClass or scenario imbalanceData lineage and reproducibility5. Controlled Experimentation and Release RecommendationsExperience designing experiments with:Clear hypothesesControlled variablesFrozen evaluation setsAcceptance thresholdsReproducible runsChampion-versus-candidate comparisonsProduction validationEvidence-based release recommendationsCandidates Must Demonstrate Ownership OfDeveloping or materially improving a production computer-vision modelAnalyzing false-positive and false-negative patternsCreating or curating image datasetsImproving annotation qualitySelecting or recommending model operating thresholdsDesigning controlled experimentsProducing model-release recommendationsValidating models after deploymentExplaining measurable improvements in model or business performanceDescriptionNext-generation item-location intelligence initiative evaluating how generated imagery can support accurate, scalable, production-ready inventory location capabilities. The program compares the existing Scrubber and IRAS production path with new processing approaches, using shared image inputs and verified physical ground truth to create a fair and auditable comparison.The Applied Computer Vision Data Scientist will own model quality, experimental rigor, dataset improvement, production error analysis, and model-release recommendations. This role will evaluate and improve computer-vision models that perform product recognition, detection, classification, localization, and related visual-perception functions using imagery captured by a five-camera system.This is not a generic data-science, LLM, NLP, forecasting, or recommendation-systems position. Candidates must demonstrate meaningful hands-on experience developing, evaluating, and improving computer-vision models used in production or production-equivalent environments.Key ResponsibilitiesDevelop, retrain, evaluate, and improve computer-vision models supporting Monarch and IRAS item-location workflows.Establish defensible model and end-to-end accuracy baselines across the Monarch evaluation arms.Analyze false positives, false negatives, recognition failures, and incorrect location results.Slice performance by club, camera role, product category, environmental condition, confidence level, and location-processing path.Evaluate exact physical-bay accuracy separately from adjusted one-bay-convention accuracy.Support the day-one physical census and daily disagreement, changed-result, and agreement-sample reviews.Design active-learning and hard-example workflows using prediction uncertainty, disagreements, production failures, and auditor feedback.Measure agreement between in-club auditors and remote image-review workflows.Determine whether errors originated in the model, image capture, rolling shutter, catalog data, annotation, pipeline, post-processing, or location-format bridging.Define model acceptance criteria for pilot and production deployment.Partner with the Pipeline Engineer to ensure inference outputs and metadata are correctly integrated.Partner with the MLOps Engineer to establish experiment lineage, model versioning, deployment controls, monitoring, and rollback criteria.Document dataset versions, model versions, experimental conditions, metrics, findings, and release recommendations.Honor the active-pilot change freeze and preserve the evidence necessary to reproduce evaluation results.Skillspython, data science, machine learningTop Skills Detailspython,data science,machine learningAdditional Skills & QualificationsSecondary Skills and Nice-to-HavesRetail computer visionProduct or SKU recognitionShelf intelligenceItem-location systemsPallet recognitionRF-DETRYOLODetectron2Vision TransformersOpenCVActive learningHard-example miningWeak supervisionMulti-camera systemsCamera calibrationRolling-shutter image analysisRobot-captured imageryMLflow or comparable experiment trackingDistributed image processingSQLSpark or RayCloud-based model trainingInter-rater agreement analysisBlinded evaluation designExperience LevelExpert Level Job Type & LocationThis is a Contract position based out of Bentonville, AR. Pay and BenefitsThe pay range for this position is $55.00 - $75.00/hr. Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to specific elections, plan, or program terms. If eligible, the benefits

available for this temporary role may include the following:

Medical, dental & vision Critical Illness, Accident, and Hospital 401(k) Retirement Plan Pre-tax and Roth post-tax contributions available Life Insurance (Voluntary Life & AD&D for the employee and dependents) Short and long-term disability Health Spending Account (HSA) Transportation benefits Employee Assistance Program Time Off/Leave (PTO, Vacation or Sick Leave) Job Type & LocationThis is a Contract position based out of Bentonville, AR. Application DeadlineThis position is anticipated to close on Nov 13, 2026. Workplace TypeThis is a fully remote position.

About TEKsystems

We're partners in transformation. We help clients activate ideas and solutions to take advantage of a new world of opportunity. We are a team of 80,000 strong, working with over 6,000 clients, including 80% of the Fortune 500, across North America, Europe and Asia. As an industry leader in Full-Stack Technology Services, Talent Services, and real-world application, we work with progressive leaders to drive change. That's the power of true partnership. TEKsystems is an Allegis Group company.

The company is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.

About TEKsystems and TEKsystems Global Services

Were a leading provider of business and technology services. We accelerate business transformation for our customers. Our expertise in strategy, design, execution and operations unlocks business value through a range of solutions. Were a team of 80,000 strong, working with over 6,000 customers, including 80% of the Fortune 500 across North America, Europe and Asia, who partner with us for our scale, full-stack capabilities and speed. Were strategic thinkers, hands-on collaborators, helping customers capitalize on change and master the momentum of technology. Were building tomorrow by delivering business outcomes and making positive impacts in our global communities. TEKsystems and TEKsystems Global Services are Allegis Group companies. Learn more at TEKsystems.com.

The company is an equal opportunity employer and will consider all applications without regard to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.

San Francisco Fair Chance Ordinance: Pursuant to the San Francisco Fair Chance Ordinance, for all positions located in the city and county of San Francisco, we will consider for employment qualified applicants with arrest and conviction records.

Massachusetts Lie Detector: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Use of Artificial Intelligence (AI): We may use Artificial Intelligence (AI) to support parts of our hiring process, including sourcing, screening, and evaluating candidates. AI helps assess applications and qualifications, but final decisions are made by our hiring team. By applying, you acknowledge and agree that your application may be reviewed using AI tools.