Computer Vision Data Scientist

You are collaborative proactive and motivated by solving complex real world problems with AI. You thrive in fastpaced crossfunctional environments balance innovation with delivery. Building production grade machine learning systems that have measurable clinical and business impact energizes you.

• Ability to Deliver Results: Proven success delivering AI solutions that improve operational efficiency product quality and user outcomes in regulated highstakes environments.
• Comfort with Ambiguity: Strong ability to structure openended problems define success metrics and make steady progress amid evolving requirements.
• Passion for Innovation: Deep interest in applied AI computer vision and emerging ML techniques with a focus on translating research into reliable products.
• Positive Outlook: Consistently finds opportunities within technical regulatory and operational constraints.
• Strong Communication Skills: Able to clearly explain complex models and results to clinicians engineers product leaders and executives.
• Bias for Action: Makes informed decisions quickly prototypes rapidly and iterates based on data and feedback.

What You Will Do

You will create and extend applied AI and ML solutions that enable intelligent real-time decisions in complex regulated environments. You will collaborate with engineering product and domain experts to deliver reliable scalable and compliant ML solutions from concept through production.

• Execute endtoend data science from problem framing and data strategy to model development deployment and optimization.
• Design train and evaluate machine learning and deep learning models for realtime and nearrealtime inference.
• Apply techniques across computer vision pattern recognition predictive modeling and generative AI to solve domain specific problems where accuracy latency and robustness are critical.
• Translate ambiguous domain and business requirements into clear methods success metrics and deployable systems.
• Build scalable data pipelines and feature engineering workflows using Python or statically compiled languages.
• Partner with engineering teams to integrate models into production systems with a focus on performance reliability and operational constraints.
• Implement monitoring validation and retraining strategies to manage drift and model performance.
• Use statistical methods and experimentation techniques to assess model quality measure impact and guide iterative improvements.
• Work within governance privacy and compliance requirements to ensure models meet organizational and regulatory standards.
• Evaluate emerging ML techniques and apply innovative approaches where they meaningfully improve outcomes.
• Improve code quality testing documentation and reproducibility across data science and ML workflows.
• Communicate technical insights and recommendations effectively to both technical stakeholders and nontechnical decisionmakers.

This Opportunity Could Be for You If

• You hold a masters degree in computer science electrical or biomedical engineering statistics or a related field with a focus on machine learning or computer vision.
• Possess a fundamental understanding of linear algebra quaternions and 3D geometry.
• Possess a fundamental understanding of the operation and nuances of image sensors lens optics and the camera calibration process.
• Experience generating intrinsic and extrinsic camera matrices and estimating camera position and pose.
• Experience optimizing algorithms for embedded systems and servers.
• You have at least three years of experience as a data scientist machine learning engineer AI research engineer or in an equivalent role.
• You have delivered data science projects from problem definition through production deployment.
• You are skilled at building ML models and working with large complex datasets.
• You collaborate effectively with crossfunctional teams and translate requirements into solutions.
• You apply advanced statistical techniques to uncover patterns trends and strategic insights.
• You proactively check model performance and pursue continuous improvement.
• You stay current on developments in AI machine learning and data engineering and look for opportunities to introduce novel approaches.

Preferred Technical Qualifications

Candidates should show strength in the following areas:

• Experience designing training validating and deploying deep learning models in production environments.
• Practical experience in the use of OpenCV and PyTorch or TensorFlow.
• Strong background in computer vision video analytics or medical image analysis.
• Ability to build scalable data pipelines and labeling workflows using modern data engineering frameworks.
• Experience deploying models using cloud and edge infrastructure with an understanding of latency and resource constraints.
• Solid grounding in statistical methods experimentation design time series analysis and model evaluation.
• Familiarity with MLOps and DevOps practices including CI/CD for ML containerization monitoring and model lifecycle management.
• Production development in Python or statically compiled languages and SQL for large scale analysis and model development.
• Experience working with regulated data and knowledge of healthcare privacy and medical device software standards is a plus.
• Effective communication skills and the ability to collaborate effectively with clinicians engineers product leaders and regulatory partners.