# Data Scientist at CodeRoad

> Remote · Latin America · Unspecified · Analytics / Data science · open. Source: https://alderhq.app/jobs/greenhouse%3Acoderoad%3A4458093009 (read 2026-11-08).

## Facts (observed)

- Company: [CodeRoad](https://alderhq.app/companies/coderoad)
- Location text: Latin America; Remote
- Where it can be done from (inferred): Remote · Latin America (evidence: "Location: Latin America; Remote")
- Posted: 2026-10-19 (19 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/coderoad/jobs/4458093009
- Skills listed (inferred): Python, PostgreSQL, SQL, LLMs

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

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?

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.

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

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.

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

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.

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.

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.

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.

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.

Maintain Data Quality & Model Performance: Implement data quality checks, validate analytical results, monitor model performance, and follow established security and governance standards across cloud data environments.

Explore & Adopt Emerging AI Tools: Proactively experiment with AI tools, libraries, and techniques to improve analytical workflows, automate repetitive tasks, accelerate experimentation, and identify new ways to solve business problems.

### What You Bring

Relevant Experience: 1–3 years of professional experience in data science, machine learning, applied statistics, or a related analytical role, with hands-on experience working on real-world datasets and analytical projects.

Python & Data Science Fundamentals: Practical experience with Python and common data science libraries such as pandas, NumPy, and scikit-learn. Familiarity with statsmodels or similar statistical tools is a plus.

SQL & Database Knowledge: Experience writing SQL queries to retrieve, transform, and analyze data from relational databases. Familiarity with Microsoft SQL Server or PostgreSQL is preferred.

Machine Learning & AI: A solid understanding of machine learning concepts, including regression, classification, clustering, and time series forecasting. Exposure to NLP, embeddings, and LLMs is highly desirable, particularly through hands-on projects or practical applications.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Acoderoad%3A4458093009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.