Staff Data Scientist / Machine Learning Scientist - Engineering Operations

We are seeking a Staff Data Scientist with a strong background in engineering for the development and deployment of innovative AI products for our multi-robot system. This multifaceted role is pivotal for our AI/ML Team, engaging the analysis, modeling, and collaboration aspects of data science with the crucial translation of data-driven insights into real-world software solutions.

As part of the AI/ML Team, you will be responsible for creating new applications and services to address complex engineering and operational challenges. An ideal candidate will have a proven track record of developing, deploying, and monitoring machine learning-based technologies for robotics applications. This role is for someone who can mentor junior developers and take ownership of software development initiatives.

What you'll do:

• Negotiate product requirements with stakeholders, manage project roadmaps and deliverables, and report progress to leadership.
• Develop robust, scalable, and high-performance software solutions that adhere to engineering best practices.
• Drive the research and development of predictive models and strategies for complex engineering and operational challenges.
• Write, test, and maintain production-level code for our AI/ML solutions, ensuring the highest standards of quality.
• Actively participate in code reviews and ensure continuous improvement in our codebase.

What we're looking for:

• Demonstrated experience in engineering roles within the industry, with a proven track record of impactful contributions.
• Demonstrated ability to develop reliable, high-quality, and maintainable software products.
• Proficiency in data analysis, statistical techniques, and machine learning methodologies in engineering domains.
• Prior experience working in a fast-paced engineering environment.

Preferred, but not required:

• MS or PhD in Mechanical/Electrical Engineering, Robotics Engineering, Operations Research, Applied Mathematics, or related field.
• MS with 7 years of professional experience, or a PhD with 3 years of professional experience, in engineering with a significant focus on AI/ML applications.
• Proficiency in MLOps principles and best practices.
• A strong foundation in causal analysis and systems modeling.
• Experience with sensor data processing, predictive maintenance, time-series anomaly detection or related experience.
• Expertise in Python, machine learning frameworks (eg. TensorFlow, PyTorch, Scikit-learn), container orchestration, cloud computing services (Azure), and query languages (SQL, KQL).