Computer Vision Data Scientist

Fortive•Minneapolis, MN

11h

About The Position

You are collaborative, proactive, and motivated by solving complex, real world problems with AI. You thrive in fast ‑ paced, cross ‑ functional 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, high‑stakes environments. Comfort with Ambiguity: Strong ability to structure open‑ended 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.

Requirements

Hold a master’s 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 cross‑functional 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.

Nice To Haves

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.

Responsibilities

Create and extend applied AI and ML solutions that enable intelligent, real-time decisions in complex, regulated environments.
Collaborate with engineering, product, and domain experts to deliver reliable, scalable, and compliant ML solutions from concept through production.
Execute end ‑ to ‑ end 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 real ‑ time and near ‑ real ‑ time 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 decision makers.