Data Scientist Job at ProSource Consulting, LLC in Bethesda
ProSource Consulting, LLC, Bethesda, MD, US
Job Description
Technical and Analytical Skills
· Advanced Statistical Analysis and Mathematics: Proficiency in probability, statistics, and mathematical modeling is crucial. This includes knowledge of hypothesis testing, statistical inference, and predictive modeling.
· Programming Languages: Proficiency in programming languages used in data science, especially Python. Knowledge of SQL for data querying is also required. Ability to write clean, readable, and efficient code.
Task 8 – Data Science and Artificial Intelligence Support
Task 8.1 – Microsoft Azure Machine Learning
Subtask 8.1.1 – Leverage Microsoft Azure Machine Learning and Python to build, test, and deploy predictive models directly in the cloud and on-premise.
Subtask 8.1.2 – Create and configure Azure ML workspaces, experiments, and compute targets to facilitate model training and evaluation.
Subtask 8.1.3 – Utilize Azure's scalable resources to optimize performance and cost-efficiency, ensuring robust and production-ready models.
Subtask 8.1.4 – Integrate models with other Azure services like Azure Data Factory and Azure Databricks to create comprehensive data solutions that support real-time analytics and decision-making across the organization.
Subtask 8.1.5 – Assist in data and server migration from on-premises SQL servers to Azure-housed datalike architectures.
Task 8.2 – Machine Learning Implementation
Subtask 8.2.1 - Design, implement, and validate complex models tailored to specific predictive/forecasting problems.
Subtask 8.2.2—Apply ensemble methods, neural networks, and advanced techniques like gradient boosting, bagging, stacking, and blending to enhance model accuracy and performance. This work will be done locally within the Python programming language, on-campus HPC resources, and on the cloud via Azure resources.
Subtask 8.2.3—Use time-series forecasting, data quality monitoring, and anomaly detection methods for industrial equipment modeling and characterization to detect failure modes, anticipate load and output, and suggest preventative maintenance.
· Advanced Statistical Methods: Deep knowledge of multivariate statistics, Bayesian inference, and experimental design. Proficiency in applying complex statistical tests and models to real-world data.