Client & Practice Area: Prudent Data & AI Practice
Experience Required: 7+ years of professional data science / applied ML experience
Location: Hybrid in the DFW area; project will being as onsite T, W, Th in Fort Worth, TX (Candidates must be local)
Role Summary:
Prudent Consulting is seeking a Senior Data Scientist to design, train, and deploy machine learning models on Dell AI Factory (Dell + NVIDIA) infrastructure. Working closely with data engineering, this role will leverage the centralized data warehouse to build production-grade ML solutions that drive measurable business outcomes. The ideal candidate brings deep applied ML expertise combined with hands‑on experience in GPU‑accelerated model development and deployment.
Key Responsibilities:
- Design, build, and validate machine learning and statistical models to address key TCU business problems.
- Train and optimize models at scale using GPU-accelerated infrastructure (Dell AI Factory, NVIDIA AI Enterprise stack), including deep learning workloads.
- Partner with data engineering to define data requirements, feature engineering pipelines, and ML-ready datasets from the centralized data warehouse.
- Deploy models into production using MLOps best practices, including versioning, monitoring, and retraining pipelines.
- Evaluate model performance, bias, and drift, ensuring responsible and explainable AI practices.
- Translate business problems into analytical frameworks and communicate findings to technical and non-technical stakeholders.
- Stay current on emerging ML/AI techniques and NVIDIA AI tooling to bring innovative solutions to the client.
- Mentor junior data scientists and contribute to reusable modeling frameworks and best practices.
Required Skills & Experience:
- 7+ years of hands‑on experience in data science, applied machine learning, or advanced analytics roles.
- Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
- Direct, hands‑on experience building and deploying models on Dell AI Factory / NVIDIA AI Enterprise GPU-accelerated infrastructure — required.
- Strong foundation in statistics, machine learning algorithms, and model evaluation techniques.
- Experience with MLOps tools and practices (e.g., MLflow, Kubeflow, model registries, CI/CD for ML).
- Experience working with large-scale structured and unstructured datasets sourced from enterprise data warehouses.
- Strong stakeholder communication skills, with the ability to translate complex analysis into business impact.
Education Requirements:
- Master's or PhD in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field (or equivalent practical experience).