Industry Data Research & Validation P18

Senior Data Science (DS/ML) Engineer

Why this opportunity is unique

The Fortive companies provide essential technology for the people who accelerate progress. Our reason for being is to bring innovations that help keep the world moving forward.

We help Fortive OpCos create customer and business value through software data and AI-enabled products and services. We work closely with operating companies to understand real-world challenges rapidly test solutions and bring scalable capabilities into production.

This role offers the opportunity to shape and deliver production-grade machine learning systems that solve meaningful business and customer problems while helping define how modern ML engineering is practiced across the organization.

Why this is a great place to work

It starts with the people and the culture. We value collaboration curiosity directness and a continuous-improvement mindset. We ask hard questions move with speed and work across teams to create practical solutions that deliver measurable outcomes.

Youll work alongside entrepreneurial high-performing colleagues across software data product and domain teams with the chance to influence both near-term product delivery and longer-term technical capabilities.

These are the traits we value:

• You are collaborative proactive adaptable and gritty with the ability to work effectively across distributed and cross-functional teams.
• You are comfortable with ambiguity and can turn loosely defined business or product questions into structured technical approaches.
• You balance technical depth with pragmatism and know how to make sound tradeoffs between speed scalability reliability and model performance.
• You have a strong bias for action and a track record of driving work through design implementation deployment and iteration.
• You communicate clearly influence effectively and help raise the capability of the teams around you.

What you will do

• Design develop deploy and scale machine learning solutions that deliver business value in production environments handling large and complex datasets.
• Partner with product managers software engineers data practitioners and business stakeholders to understand problems define success criteria and translate requirements into robust machine learning systems.
• Build and maintain data feature training inference and evaluation pipelines that support reliable end-to-end model development and deployment.
• Explore and assess data quality distributions patterns and signal to improve feature design model performance and overall solution reliability.
• Develop and implement approaches for feature engineering predictive modeling performance evaluation and model improvement using appropriate machine learning methods.
• Design and run rigorous experiments to compare approaches evaluate tradeoffs and guide decisions on model pipeline and system improvements
• Productionize models through sound software engineering and MLOps practices including CI/CD/CT containerization testing release readiness and service integration
• Establish monitoring and diagnostics for model behavior in production including drift detection performance tracking retraining triggers troubleshooting and lifecycle management.
• Contribute to the design of scalable APIs services and platform components that enable ML capabilities to be reused across products and teams
• Recommend best practices and technical improvements based on internal needs external developments and hands-on experience delivering ML systems at scale.
• Lead or significantly influence projects with meaningful technical scope and serve as a mentor and resource for other engineers and practitioners.

This opportunity could be for you if

• You hold a bachelors degree in computer science engineering mathematics statistics or a related quantitative field; an advanced degree is a plus.
• You have strong experience building and deploying end-to-end machine learning solutions in production.
• You bring strong foundations in machine learning algorithms statistics data structures and software engineering.
• You are highly proficient in Python and comfortable working with modern ML and data libraries/frameworks.
• You have experience with cloud-native and distributed systems used in ML workflows such as AWS/Azure/GCP Docker Kubernetes Spark SQL and related data platforms
• You are comfortable working across the full model lifecycle: data preparation feature creation model training evaluation deployment monitoring and iterative improvement.
• You know how to evaluate model quality using sound metrics error analysis and experimental methods and can use evidence to drive improvements.
• You can explain technical concepts clearly to technical and non-technical stakeholders and influence decisions through structured reasoning and strong communication.
• You are effective in ambiguous environments and able to work independently while collaborating across multiple teams and functions.

Preferred qualifications:

• Advanced degree in computer science engineering mathematics statistics or a related discipline.
• Experience designing ML systems that support large-scale or high-reliability applications.
• Experience with deep learning and/or domain-specific ML applications such as time-series NLP computer vision or optimization-based solutions.
• Experience building reusable platform capabilities APIs or shared services for ML-enabled products.
• Strong judgment in selecting the right level of model complexity for the business problem data realities and operational constraints.
• Demonstrated ability to mentor others improve technical practices and help teams adopt scalable ways of working.