> Markdown version of [/jobs/ext/3959185-sr-data-scientist](https://www.wearedevelopers.com/jobs/ext/3959185-sr-data-scientist). 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). --- # Sr. Data Scientist - **Company:** PROLIM Global Corporation - **Location:** Austin, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Data Analysis, Computer Programming, R (Programming Language), Python (Programming Language), Machine Learning, Power BI, Tensorflow, SQL Databases, Tableau (Software), Scripting, Pytorch, Scikit Learn - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/sr-data-scientist-austin-tx--38487455-dda2-4076-9029-31eb6e649247 ## About the Role Data Science, Python Programming/Scripting Language, R Programming Language, SQL (Structured Query Language), Mathematics, Statistics, Linear Algebra, Machine Learning, Algorithms, Data Collection, Programming: Proficiency in Python, R, and SQL. Mathematics/Statistics: Strong understanding of statistical techniques, probability, and linear algebra. Machine Learning: Knowledge of algorithms and libraries like Scikit-learn, TensorFlow, or PyTorch. ## Description Data Collection and Preparation: Gathering unstructured and structured data from multiple sources, then cleaning and preprocessing it to ensure data quality and usability. Exploratory Data Analysis (EDA): Analyzing data to uncover hidden patterns, correlations, and trends. Modeling and Machine Learning: Developing algorithms, designing predictive models, and training them on historical data for tasks like classification, regression, and optimization. Data Visualization and Reporting: Using tools (e.g., Tableau, Power BI) to create reports and charts that communicate technical findings to non-technical stakeholders. Collaboration and Strategy: Working with business leaders to identify goals, define problems, and suggest data-driven improvements. Model Maintenance: Testing, validating, and updating models to ensure accuracy and relevance over time.