Staff Data Scientist

Employment Type: [Not Specified] Location Type: [Not Specified] Salary: [Not Specified]

Who We Are

At Platform Science, we’re working to connect everything that moves. Founded in 2015, we are an open IoT platform that partners with innovative fleets, application developers, vehicle manufacturers, and equipment providers in the transportation industry to deliver revolutionary solutions to supply chain professionals across the globe.

Our employees are an engaging, diverse group of people who believe in the power of great ideas. We hire people with different experiences and perspectives to build a company culture that fuels growth through innovation. We value thoughtful actions and empathy for others. We approach challenges with resiliency and creativity, while encouraging transparency because, no matter our backgrounds or responsibilities, we are one team.

About the Role

We are looking for a Staff Data Scientist to help us process large amounts of raw data into strategic insights for consumption by our Product, Engineering, and Leadership teams. In this role, you will lead the evaluation, design, and implementation of machine learning models and statistical analyses to support key business objectives.

As a Staff Data Scientist, you will play a critical role in driving data-driven decision-making at Platform Science and shaping the future of the company’s data science and machine learning capabilities. You have excellent communication skills, both written and verbally, with proven experience communicating highly technical information to a variety of audiences.

Essential Responsibilities

• Drive the full machine learning lifecycle as a key technical leader. Shape architectural decisions, guide system design and model selection, and oversee the deployment of scalable, production-ready models using robust MLOps practices.
• Own and continuously improve ML systems in production, working with engineering to ensure high availability, reliability, and performance. Define standards for observability, monitor model drift, and implement retraining and alerting pipelines.
• Partner with our Product and Engineering teams to translate ambiguous problems into clear, actionable requirements. Scope and prioritize ML opportunities that deliver measurable business value.
• Apply advanced data mining and machine learning techniques, such as natural language processing, deep learning, and neural networks, to solve complex business problems.
• Proactively engage with senior leadership to identify opportunities where data science can drive innovation, efficiency, or competitive differentiation. Influence product and business strategy through data-driven insights.
• Regularly communicate findings and recommendations clearly and effectively to both technical and non-technical audiences, tailoring content to the stakeholder context.
• Collaborate with Data team members and Product leaders to shape the data science strategy and roadmap for Platform Science. Lead the organization in establishing best practices for launching and analyzing ML products.
• Mentor, coach, and level up peers and stakeholders, fostering a culture of continuous learning and technical excellence.

Experience

• 7+ years of experience in a data science or machine learning role.
• 3+ years experience in mentorship or leadership roles, with the proven ability to provide guidance and support as a subject matter expert to other data team members and stakeholders.
• Demonstrated experience leading ML initiatives from conception through production across multiple business domains.
• Proven track record of designing, building, deploying, and maintaining production-grade machine learning systems using modern MLOps practices.
• Deep understanding of machine learning libraries (scikit-learn, TensorFlow, etc.) and algorithms.
• Strong foundation in statistics and applied statistical analysis, including A/B testing, hypothesis testing, Bayesian inference, time series modeling, and multivariate methods.
• Proficient in Python and SQL, with extensive experience in data manipulation, analysis, and visualization.