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# Data Scientist

- **Company:** Genesis10
- **Location:** Charlotte, NC, United States
- **Salary:** $114,525.0 - $131,165.0
- **Contract:** Temporary contract
- **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Cluster Analysis, Data Visualization, Relational Databases, Github, Apache Hadoop, Statistical Hypothesis Testing, Python (Programming Language), Logistic Regression, Machine Learning, NumPy, Power BI, Tensorflow, Azure Machine Learning, SciPy, SQL Databases, Tableau (Software), Unstructured Data, Data Processing, Google Cloud, Pytorch, Retrieval-Augmented Generation, Snowflake, Random Forest, Deep Learning, Gitlab, Git, Pandas, Matplotlib, Pyspark, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations, Looker Analytics, Unsupervised Learning
- **Published:** October 31, 2026
- **Apply:** https://www.dice.com/job-detail/b61c742b-1163-4e97-a335-e56bd2f05434

## About the Role

We are seeking an analytical, detail-oriented, and innovative Data Scientist to join a growing analytics team. In this role, you will analyze complex, high-dimensional datasets to create actionable insights, build predictive models, and design machine learning algorithms that drive critical business decisions. The ideal candidate has strong hands-on expertise in statistical modeling, machine learning, data manipulation, and translating technical findings into clear business strategies., * Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Economics, or a related quantitative field

 * Advanced proficiency in Python or R (libraries such as Pandas, NumPy, Scikit-Learn, SciPy, Matplotlib/Seaborn)
 * Strong hands-on expertise with SQL (complex joins, CTEs, window functions)
 * Solid understanding of supervised and unsupervised learning algorithms (Linear/Logistic Regression, Decision Trees, Random Forests, XGBoost, Clustering, KNN)
 * Strong foundation in probability, inferential statistics, linear algebra, and hypothesis testing
 * Proficiency with Git and GitHub/GitLab workflows

Desired skills:

 * Experience with cloud platforms (AWS, Google Cloud Platform, or Azure) and managed ML services (Sagemaker, Vertex AI)
 * Familiarity with Big Data frameworks (PySpark, Hadoop, or Snowflake)
 * Experience with Deep Learning frameworks (TensorFlow, PyTorch) or Large Language Model (LLM) fine-tuning / RAG workflows
 * Exposure to MLOps principles and model tracking tools (MLflow, DVC)
 * Hands-on experience with visualization tools like Tableau, Power BI, or Looker

## Description

* Extract, clean, transform, and aggregate large-scale structured and unstructured data from disparate sources (relational databases, APIs, logs)
 * Design, build, evaluate, and deploy scalable predictive, classification, and clustering models using statistical and machine learning techniques
 * Perform deep-dive statistical analysis to identify trends, patterns, correlations, and anomalies in complex datasets
 * Partner closely with business stakeholders, product managers, and software engineers to understand key business drivers and turn requirements into data science solutions
 * Work with Data Engineers and MLOps teams to integrate models into production environments and monitor model drift and performance over time
 * Synthesize technical insights into executive summaries, dashboards, and visual presentations for non-technical leadership
 * Design, execute, and analyze statistical experiments (A/B testing, hypothesis testing) to validate feature performance and strategy changes