> Markdown version of [/jobs/ext/4020682-data-scientist-usa-remote](https://www.wearedevelopers.com/jobs/ext/4020682-data-scientist-usa-remote). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist - USA (Remote)

- **Company:** COGNIFY ANALYTICS INC.
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Salary:** $109,200.0 - $120,900.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Neural Networks, Big Data, Data Mining, Apache Hive, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Recommender Systems, SQL Databases, Text Mining, Feature Engineering, Apache Spark, Pandas, Scikit Learn, Information Technology, Data Pipelines, Unsupervised Learning
- **Published:** October 30, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=32b4e70ff1bd0f11

## About the Role

* Bachelor's, Master's, or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, Data Science, or a related quantitative field.
 * At least 4 years of professional industry experience in applied data science, statistical modeling, and machine learning.
 * Advanced proficiency in Python and standard data science libraries such as pandas, NumPy, and scikit-learn.
 * At least 4 years of hands-on experience querying and processing large-scale datasets with SQL, Hive, and Spark or SparkSQL.
 * Demonstrated track record building classification, regression, decision trees, neural networks, recommender systems, sequential pattern discovery, and applied NLP or text mining models.
 * Strong foundation in hypothesis testing, experimental design, and A/B testing analysis.
 * Clear communication skills with the ability to explain complex mathematical, statistical, and modeling concepts to both technical and non-technical stakeholders.

## Description

* Own the end-to-end data science lifecycle from exploratory analysis and feature engineering to model building, validation, and production integration.
 * Apply supervised and unsupervised learning, data mining, and NLP techniques to build recommender systems that improve the user journey.
 * Extract, clean, and transform large distributed datasets by authoring optimized data pipelines using SQL, Hive, and SparkSQL.
 * Design, build, and iterate on high-signal predictive features from raw transactional, behavioral, and text data.
 * Design, execute, and analyze controlled A/B tests and experiments, translating statistical findings and metrics into actionable business recommendations.
 * Partner directly with engineering, product, and design teams to build working MVPs, align on technical requirements, and test product hypotheses.