Data Scientist

About the role

Engagement Type: [Full-Time / Contractor]

About CodeRoad

CodeRoad is a technology execution partner trusted by companies across North America since 2002. We work alongside our clients to engineer, modernize, and scale the technology that powers their businesses, combining deep technical expertise, specialized teams, and shared accountability for outcomes. Our capabilities span product engineering, platform engineering, legacy modernization, and AI engineering.

Our engineers, designers, delivery leaders, and technology specialists collaborate through Velocity Studios: specialized teams developed within CodeRoad to deliver complex technology initiatives. Being assigned to a project means you become part of a collaborative environment where you can strengthen your expertise, take ownership of meaningful work, and grow alongside experienced professionals.

Why Build Your Career at CodeRoad?

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High-Velocity Work & Client Exposure: We work close to the technology, teams and roadmaps that move real outcomes. Direct engagement with U.S. client teams and roadmaps.

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Career Continuity & Long-Term Opportunity: Your career here can outlast any single project. Continuous multi-project pipeline; long-tenure CodeRoaders.

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Engineering Autonomy & Technical Mastery: We invest in the skills that shape what's next. Agentic AI tools and training, Coursera Enterprise, Studio practice labs, L1-L7 Career Framework.

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High-Trust Employee Experience: You grow in an environment built on trust, transparency and recognition. High-touch onboarding, transparent leadership communication, community recognition, psychological safety.

Key Responsibilities

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Develop & Improve Machine Learning Models: Build, test, and refine machine learning and statistical models to identify anomalies, forecast costs, analyze shipping rates, and classify invoices. Evaluate model performance and contribute to improving predictive accuracy.

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Apply AI, NLP & LLM Techniques: Explore and apply Natural Language Processing (NLP), embeddings, and Large Language Models (LLMs) to extract meaningful insights from unstructured supply chain documents, logistics contracts, and other business data.

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Build & Maintain Data Pipelines: Collaborate with AI and software engineers to prepare datasets, develop feature engineering workflows, and support model deployment, monitoring, and automated retraining.

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Conduct Exploratory & Scenario Analysis: Clean, transform, and analyze transactional datasets to identify patterns, evaluate shipping trends, compare carrier rates, and support cost optimization and contract negotiation decisions.

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Translate Data into Business Insights: Use SQL, statistical analysis, and data visualization to communicate findings clearly and turn complex data into actionable recommendations for business stakeholders.