Applied Computer Vision Data Scientist

Next-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., Develop, 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.

Scalability Forecasting Localization Data Science Data Analysis Visual Acuity Image Quality Failure Causes Experimentation Floor Scrubbers Post Processing Computer Vision Active Learning Machine Learning Business Valuation Course Evaluations Model Architecture Workflow Management Software Versioning Confidence Intervals, Hands-on experience building and evaluating models for at least one of the following:

Object detection

Image classification

Product or visual recognition

Object localization

Visual search or matching

Multi-camera perception

Image-based inventory or location intelligence

Candidates should be able to explain the model architecture, dataset, evaluation methodology, release path, production failure modes, and measured outcome.

1. Python and Modern ML Frameworks

Strong Python development with hands-on experience using:

PyTorch, Candidates must be able to move beyond notebooks and contribute maintainable, tested code supporting model training, evaluation, data analysis, and production validation.

1. Production Model Evaluation and Error Analysis

Deep understanding of:

Precision and recall

False positives and false negatives

Confidence thresholds

Calibration

Confusion matrices

Error slicing

Segment-level performance

Ground-truth comparison

End-to-end business accuracy

Monarch specifically requires candidates who can distinguish model failures from image-quality, annotation, camera, catalog, pipeline, post-processing, and location-conversion failures.

1. Dataset Development and Annotation Quality

Hands-on ownership of:

Dataset creation and curation

Training, validation, and test-set design

Annotation guidelines

Label-quality audits

Hard-example identification

Dataset versioning

Deduplication

Class or scenario imbalance

Data lineage and reproducibility

1. Controlled Experimentation and Release Recommendations

Experience designing experiments with:

Clear hypotheses

Controlled variables

Frozen evaluation sets

Acceptance thresholds

Reproducible runs

Champion-versus-candidate comparisons

Production validation

Evidence-based release recommendations

Candidates Must Demonstrate Ownership Of

Developing or materially improving a production computer-vision model

Analyzing false-positive and false-negative patterns

Creating or curating image datasets

Improving annotation quality

Selecting or recommending model operating thresholds

Designing controlled experiments

Producing model-release recommendations

Validating models after deployment

Explaining measurable improvements in model or business performance, python, data science, machine learning

Top Skills Details

python,data science,machine learning

Additional Skills & Qualifications

Secondary Skills and Nice-to-Haves

Retail computer vision

Product or SKU recognition

Shelf intelligence

Item-location systems

Pallet recognition

RF-DETR

YOLO

Detectron2

Vision Transformers

OpenCV

Active learning

Hard-example mining

Weak supervision

Multi-camera systems

Camera calibration

Rolling-shutter image analysis

Robot-captured imagery

MLflow or comparable experiment tracking

Distributed image processing

SQL

Spark or Ray

Cloud-based model training

Inter-rater agreement analysis

Blinded evaluation design, 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.

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., 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.