Data Scientist

v (only USC)

Location: Dallas, TX

Work Model: Hybrid

Role Overview

We are seeking an analytical and results-driven Contract Data Scientist to join our team in Dallas, TX. In this role, you will analyze large-scale datasets, build and validate predictive models, and deliver actionable insights to solve complex business problems. This position operates on a hybrid schedule , requiring 3 days onsite per week at our Dallas facility.

Key Responsibilities

• Exploratory Data Analysis & Modeling: Clean, preprocess, and analyze large datasets to develop machine learning algorithms, statistical models, and predictive frameworks.

• Pipeline & Feature Engineering: Design and refine robust data pipelines and feature sets to optimize model efficiency and performance.

• Business Insights & Visualization: Translate complex model outputs and data analyses into intuitive visualizations, reports, and strategic recommendations for stakeholders.

• Model Validation & Maintenance: Continuously test, validate, and monitor model performance in production to ensure accuracy, fairness, and reliability over time.

• Cross-Functional Collaboration: Partner with data engineering, product management, and business domain teams to align data science initiatives with core organizational objectives.

Qualifications & Requirements

• Experience: 3 5+ years of hands‑on experience in data science, predictive modeling, and applied machine learning within a business or enterprise environment.

• Technical Proficiency:

  • Strong programming skills in Python or R, alongside advanced SQL for complex queries and data extraction.

  • Practical experience with core data science libraries (e.g., Pandas, NumPy, Scikit-learn, XGBoost) and deep learning frameworks (PyTorch or TensorFlow).

  • Familiarity with data visualization tools (Tableau, Power BI, or Matplotlib/Seaborn).

  • Cloud platform experience (AWS, Azure, or GCP) for running model workflows and managing data.

Preferred Qualifications

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Applied Mathematics, or a related quantitative field.

• Prior experience working in financial services, healthcare, or corporate technology environments.

• Knowledge of standard software engineering practices (Git, version control, CI/CD).