Role Summary

Summit Technical is seeking an experienced Data Scientist to manage the day-to-day modeling, reporting, predictive analysis, and optimization of our critical business and operational data. Reporting directly to the Director of Analytics, you will be responsible for the development, security, operations, and maintenance of our data pipelines and machine learning infrastructure.

Key Responsibilities

• Data & Pipeline Lifecycle Management: Oversee cloud-based and onsite data ingestion, storage, and cleaning workflows, while maintaining feature stores and deployment configurations for machine learning pipelines.
• Predictive Modeling & Governance: Implement predictive models and anomaly detection algorithms, while coordinating routine model retraining, software updates, and performance tracking with leadership.
• Analytics & Stakeholder Support: Configure business intelligence dashboards, telemetry reports, and video analytics systems, while providing daily technical support and data access protocols to local and remote decision-makers.
• Reporting, Training & Compliance: Deliver routine model performance reports, train internal teams on analytics tools, and ensure strict compliance with enterprise data governance, ethics, and privacy laws.

Required Qualifications

• Experience & Resilience: Minimum of 5 years of experience as a data science professional with a background in data engineering or statistical modeling, including a proven ability to work effectively under tight deadlines and high-pressure conditions.
• Technical Depth & Machine Learning: Thorough knowledge of programming languages (Python, R), SQL database administration, and advanced statistical analysis, alongside core machine learning frameworks like Scikit-Learn, TensorFlow, and PyTorch.
• Data Infrastructure & Cloud Platforms: Proven experience building and troubleshooting ETL and data deployment pipelines across major cloud data platforms (AWS, Azure, or GCP).
• Analytical Agility & Insights: Highly mentally agile and capable of working independently to process complex, unstructured datasets and successfully translate them into clear, actionable business insights.
• Professionalism & Compliance: Exceptional work ethic and interpersonal skills to communicate complex data concepts to senior management, staff, and vendors, with the ability to obtain data governance certifications within one year of hire.

Desired Qualifications

• Education & Domain Experience: MS or PhD in Data Science, Computer Science, Statistics, Operations Research, or a related field, paired with a background in technical operations, logistics, or industrial systems analytics.
• Data Architecture & Network Leadership: Proven experience as a lead data architect on enterprise-level data mesh networks, including the optimization of large-scale server clusters.
• MLOps & Infrastructure: Enterprise-level expertise utilizing MLOps frameworks (MLflow, Kubeflow) and managing containerization platforms like Docker.
• Streaming & Edge Systems: Thorough knowledge of high-throughput streaming data architectures (Apache Kafka, Spark) alongside the ability to maintain automated edge-computing data scripts.
• Professional & Agile Certifications: Active cloud and project credentials, including AWS Certified Data Analytics, Google Cloud Professional Data Engineer, baseline Data Security, and Agile/Scrum certifications.

In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire. All jobs are subject to random drug screening.