Staff Data Scientist

(Hybrid 4-days a week onsite or remote for the right candidate)

Position Summary

The Staff Data Scientist will be a key role in the Data Science and Analytics team tasked with providing technical leadership for the establishment of enterprise wide capabilities in data science, AI and predictive analytics. The Staff Data Scientist will typically work on 3–5 large projects concurrently that have organization-wide impact. In addition to these projects, the Staff Data Scientist will provide technical consultation, advice and training on all major on-going Data Science and Analytics projects. When required, the Staff Data Scientist will also act as a project manager where vendors, suppliers and consultants are engaged on key strategic and emerging technology initiatives.

Major Responsibilities

Identifying High Value Analytics & AI Opportunities

  • Partner with business leaders to identify opportunities where predictive analytics, machine learning, or generative AI can improve productivity, reduce cost, or unlock new capabilities.

  • Develop clear business cases and ROI models to prioritize initiatives and communicate value to senior leadership.

Lead Data Science Projects

  • Translate complex business requirements into robust, scalable technical solutions.

  • Select and implement appropriate modeling techniques, including classical ML, deep learning, generative AI, and reinforcement learning where applicable.

  • Oversee the full model lifecycle: data exploration, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement.

  • Ensure solutions are production ready, maintainable, and aligned with MLOps best practices.

  • Drive organization wide adoption of models and AI systems through clear communication, documentation, and stakeholder engagement.

Technical Guidance & Thought Leadership

  • Provide expert consultation on ML algorithms, model tuning, experimentation frameworks, and cloud native data engineering patterns.

  • Mentor data scientists, ML engineers and AI engineers; support skill development in areas such as forecasting, ML modeling, generative AI, vector databases, and modern ETL/ELT workflows.

  • Contribute to the development of internal standards, reusable components, and best practice guidelines.

Project Management

  • Develop and maintain project plans, milestones, and communication strategies for strategic initiatives.

  • Facilitate regular updates with stakeholders, executives, and cross functional partners.

  • Coordinate with vendors, consultants, and technology partners when external expertise is required.

Lead Technology Change in Data Science, Analytics and AI

  • Evaluate emerging technologies including generative AI platforms, MLOps tools, cloud services, and data engineering frameworks to determine applicability and business value.

  • Recommend and influence adoption of modern, flexible, and scalable technologies that support a unified enterprise data and AI platform.

  • Drive experimentation and prototyping to accelerate innovation and reduce time to value.

Qualifications

  • Master's Degree required; preferred concentrations in Engineering, Operations Research, Statistics, Applied Math, Computer Science, Data Science or related quantitative field.

  • PhD preferred in Engineering, Operations Research, Statistics, Applied Math, Computer Science, Data Science or related quantitative field.

  • 7+ years of experience along with a PhD in a related field OR 10+ years of experience along with a Master's degree in a related field required.

  • Advanced experience developing and deploying machine learning models using Python and modern ML frameworks (e.g., Scikit-learn, PyTorch, TensorFlow).

  • Strong applied expertise across core ML techniques, including regression, tree based models, clustering, deep learning, and NLP.

  • Familiarity with generative AI and LLMs, including prompt engineering, fine-tuning, embeddings, and vector databases.

  • Solid understanding of MLOps practices, including CI/CD for ML, automated training pipelines, model versioning, monitoring, and model governance.

  • Hands-on experience with cloud based ML platforms (AWS, Azure, or GCP) and containerization/orchestration tools such as Docker and Kubernetes.

  • Working knowledge of modern data ecosystems (Snowflake, Redshift) and the ability to collaborate effectively with data engineering teams when needed.

  • Advanced skill in statistical modeling, SQL, and database concepts required.

  • Demonstrated experience leading small technical teams or pods, providing mentorship and technical direction.

  • Familiarity with Logistics industry is preferred.

  • Regular, predictable, full attendance is an essential function of the job.

  • Willingness to travel as necessary, work the required schedule, work at the specific location required, complete our client's employment application, submit to a background investigation (to include past employment, education, and criminal history) and drug screening are required.

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