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# Lead Data Scientist

- **Company:** HERE
- **Location:** Chicago, IL, United States (Remote available)
- **Experience:** Expert
- **Salary:** $160,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Data Auditing, Machine Learning, System Testing, Visual Systems, Delivery Pipeline, Deep Learning, Generative AI, Data Strategy, Information Technology
- **Published:** July 3, 2026
- **Apply:** https://careers-here.icims.com/jobs/81685/lead-data-scientist/job?ss=1&mode=job&iis=BuiltInNationwide&iisn=BuiltInNationwide

## About the Role

Strong background in applied machine learning, computer vision, synthetic-data evaluation, or perception-system validation

 - Experience designing metrics and evaluation frameworks for generative, simulation, or perception systems
 - Experience connecting model behavior, data quality, and product outcomes in ambiguous AI systems
 - Ability to translate research-quality experiments into practical engineering and release decisions
 - Strong analytical judgment and clear written communication
 - Comfort owning both strategy and execution in a small team
Education & Experience

 - Master's or PhD in Computer Science, AI, Machine Learning, or related field.
 - 5-8 years of experience in deep learning, computer vision, or multimodal AI.
Nice To Have

 - Experience with simulation, autonomous systems, geospatial AI, or map-grounded perception tasks
 - Familiarity with video quality metrics, structural similarity measures, temporal consistency checks, segmentation and detection evaluation, or label-quality assessment
 - Experience assessing synthetic-to-real transfer, dataset usefulness for downstream models, data curation strategy, or production quality governance

## Description

We are hiring a Lead Data Scientist to own applied AI quality, data strategy, and downstream usefulness across advanced AI, computer vision, and perception systems. This person will define how we determine whether a system is working, where it is failing, what quality bar is required for release, and how data, evaluation, and applied modeling should evolve to improve the product.
This is a senior role for someone who can bring rigor to ambiguous technical programs, establish evaluation systems, and translate model behavior into product decisions, data strategy, and concrete improvement loops.
What You Will Own

 - Own the evaluation framework and quality strategy for advanced AI and vision systems
 - Define pass/fail metrics for output quality, structural fidelity, temporal consistency, label quality, robustness, and operational repeatability
 - Own data and validation strategy for improving model quality and downstream usefulness
 - Lead artifact auditing, failure taxonomy development, release-quality reporting, and evidence-based prioritization
 - Measure whether outputs are suitable for perception, mapping, generative AI, and customer-facing use cases
 - Partner with model, simulation, and platform owners to drive quality improvements and production-readiness decisions
What You Will Do

 - Build and evolve metric suites for output quality, fidelity, repeatability, and downstream usefulness
 - Define human-review protocols and product acceptance thresholds for complex AI systems
 - Evaluate whether outputs preserve the structure, semantics, and consistency expected by downstream applications
 - Translate evaluation findings into data strategy, experiment priorities, and applied modeling opportunities
 - Help define dataset design, validation slices, and quality-improvement loops across the product
 - Create experiment and release reports that turn technical output into clear product decisions
 - Help prioritize what the team should fix next based on evidence rather than intuition
 - Establish evaluation foundations that remain useful across future AI, perception, and mapping capabilities, Artificial Intelligence * Automotive * Computer Vision * Information Technology * Internet of Things * Logistics * Software
Lead development of map-grounded world foundation models and generative scenario synthesis for AVs. Own end-to-end ML lifecycle, bridge generative models with classical simulators, define synthetic data quality and validation frameworks, run POCs with partners, and enable sim-to-real strategies to improve perception and planning.
Top Skills: AlpasimAsam OpenxCarlaCosmos-TransferDiffusion ModelsIso 26262Iso 34502Latent Video ModelsLidarNvidia CosmosNvidia Drive SimOpendriveOpenscenarioPythonPyTorchPytorch LightningSotifTransformer-Based World ModelsUnityUnreal
HERE Technologies


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