Role: Data Scientist Duration: 12 Month Location: St. Louis, MO (Fully Onsite)
Job Description:
Seeking a highly analytical Data Scientist to join our Data Center Group.
Responsible for extracting meaningful insights from massive, disparate datasets to optimize our data center operations, improve resource allocation, and drive strategic decision-making.
This role requires a unique blend of statistical expertise, programming proficiency, and a deep understanding of diverse database architectures.
Key Responsibilities:
Data Acquisition & Integration: Extract, clean, and integrate data from various "flavors" of databases, including relational (SQL) and non-relational (NoSQL) systems.
Exploratory Data Analysis (EDA): Perform deep-dive analyses to identify patterns, anomalies, and trends within data center metrics such as power usage, cooling efficiency, and server performance.
Advanced Modeling: Develop, test, and deploy machine learning models and predictive algorithms to forecast infrastructure needs and automate operational processes.
Data Visualization: Create intuitive dashboards and reports to communicate complex findings to both technical teams and executive stakeholders.
Cross-Functional Collaboration: Partner with data engineers to optimize data pipelines and ensure data quality across our global data center footprint.
Required Qualifications
Education: Bachelor s or Master s degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
Technical Skills:
Proficiency in Python or R for data manipulation and modeling.
Advanced SQL skills for querying complex relational databases.
Experience with NoSQL databases (e.g., MongoDB, Cassandra) and big data frameworks (e.g., Apache Spark).
Strong foundation in Statistics and Probability.
Soft Skills: Excellent communication, problem-solving, and the ability to translate technical data into business-relevant narratives.
Preferred Experience:
Prior experience working within a data center or large-scale infrastructure environment.
Familiarity with cloud computing platforms (AWS, Google Cloud Platform).
Experience with MLOps for productionizing and monitoring models.
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