Summer Associate Internship​/Operational TechnologiesData Analyst

Position: Summer Associate Internship (Operational TechnologiesData Analyst)

Overview

Operational Technologies (OT) is a team within the Service Transition branch of NFCU’s Information Services Department (ISD). The team’s primary responsibility is working with IT, industrial Internet of Things (IoT), and operational technology teams to lead technology driven business process transformation and innovation for Navy Federal Credit Union. We partner with IT, Risk, Security, and Audit teams to establish operational compliance that streamlines operations while protecting the organization. Team members possess Industry and Technology acumen, and are familiar with business systems (IT), plant operations environment (OT), Cyber Security, Network Security, and the ability to connect these domains to form the enterprise solutions approach. The Summer Associate Program is a 12-week internship program beginning in May 2024 and ending in August 2024. Students will work on impactful projects and meaningful work during their internship. To qualify for this position, applicants must be currently pursuing a degree from an accredited college or university and have an anticipated graduation date of December 2024 or later. Potential Projects: OT data analysts/scientists must understand the differences in the processing and management of data on the edge, where OT happens, versus traditional infrastructure. Project #1 (12-week plan):

• Provide a developmental plan of predictive analytics for OT devices through data ingested by the OT software. The analytics should provide a way of identifying potential problems with the environment or equipment minimizing issues
• Create customized reporting from data collected from the OT software tool and provide a way to automate/schedule reporting
• Collaborate with Service Now to ensure data ingestion is migrating into Service Now accurately. Resolve all discrepancies
• Represent OT Management on various enterprise governance boards, as needed to assure requirements are addressed in updates to existing enterprise environment, policies/standards, and projects

Project #2 (12-week plan):

• Develop analytics for use by the Operational Technology Management and dissemination to other ISD teams for strategic decisions
• Assist with internal projects that may include teaching, documenting, or automating processes relevant to Operational Technology Management
• Represent OT Management on various enterprise governance boards, as needed to assure requirements are addressed in updates to existing enterprise environment, policies/standards, and projects
• Support other teams within the Service Transition umbrella

Responsibilities

• The skills an OT data analyst/scientist must have: All data analysts/scientists should be well-versed in machine learning and deep learning, but OT data analysts/scientists also require different skills from traditional data scientists.
• Identifies relevant data sets needed for data analytic applications
• Works with others to collect, integrate, and prepare data for analysis
• Develops and runs analytical models and assesses the findings
• Communicates the results to business owners, users, and others as needed
• Preprocessing of data:

Data doesn’t flow out of OT in tidy, well-formatted records, as it does in conventional systems. OT data is often sparse or incomplete, subject to the whims of the environment and the state of the machine producing it and it varies under changing conditions. The data is frequently temporal and time sensitive. OT data analysts/scientists can apply deep learning to spot conditional shifts in data patterns, make predictive assessments of data quality, and fill in the gaps as needed.

• Sensor fusion: Increasingly, the state of a machine or a process depends on many OT sensor inputs. The challenge is to integrate the data from disparate devices meaningfully to boost the quality and mitigate the uncertainty of individual results. OT data analysts/scientists often must customize data integration, which requires a specialized methodology to achieve and validate.
• Real-time processes: Another major consideration is the need to aggregate and correlate OT data for real-time processes. OT data is often unstructured and must be tagged and correctly synchronized in real time for proper use because time windows fluctuate, and some applications require instantaneous best-guess corrections. OT data analysts/scientists will analyze unstructured data.
• An understanding of signal processing: Data streaming into enterprise processes via OT channels should be treated as a complex set of signals and switches all working together to produce a desired result. The timing and relative strength of the signals are crucial to making sense of the intelligence they can convey. OT data analysts/scientists will apply competence with signal processing mathematics, as well as information theory, which are major advantages in making sense of OT data.
• Knowledge of gateway layers: Between the edge and the enterprise there should always be a gateway layer where security, routing, and often data aggregation…

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