Analyst-Lead

Job Title

Analyst-Lead

Location

ARL Maryland - Adelphi, MD 20783 US (Primary)



Fibertek is seeking a Data and Process Analyst to support our team at the Army Research Lab. As the Data and Process Analyst for the Army Research Laboratory (ARL), you will be a cornerstone for our programs' management and administrative support. This will involve collecting, processing, and performing statistical analyses on large datasets to discover useful information, suggest conclusions, and support decision-making. It encompasses various techniques under various umbrellas, such as descriptive statistics, exploratory data analysis (EDA), and inferential statistics, to interpret and understand the patterns and behaviors within data. Data analysis aims to extract actionable insights from raw data that can influence strategies and operations. You will also streamline business processes and procedures to obtain, collect, process, and interpret the data to improve the outcomes and performance of the team, participants, and customers. Your role will involve close collaboration with regional teams to bolster the effectiveness of ARL Educational Outreach Office programs and activities.

As the Process Analyst, you will review, analyze, and evaluate business systems and operational needs. You will document requirements, define scope and objectives, and formulate systems to parallel overall business strategies. You will coordinate with departmental heads, executives, and other stakeholders to streamline processes, enhance productivity, and improve operational effectiveness.

Essential Duties and Responsibilities:


Collects, processes, and performs statistical analyses on data sets, including:

1. Data Cleaning and Preparation:
   This involves filtering the data, handling missing values, and preparing the dataset for analysis to ensure accuracy and relevance.
2. Data Exploration and Analysis:
   Analysts use statistical tools and techniques to explore and analyze data, identifying patterns, relationships, and trends.
3. Data Visualization:
   They create visual representations of data findings through charts, graphs, and dashboards to make the data understandable at a glance.
4. Reporting:
   Data analysts prepare reports and presentations to communicate the insights and findings from the data to stakeholders, which can influence policy and decision-making processes.
5. Collaboration:
   They often work with other departments to understand their data needs and help them make informed decisions based on data insights.

• Gather data from primary and secondary sources, ensuring the upkeep of databases and data systems.
• Detect, examine, and decode trends or patterns within intricate datasets.
• Cleanse data and scrutinize computer-generated reports and outputs to identify and rectify coding errors.
• Coordinate with management to align business and informational priorities.
• Identify opportunities for process enhancements.
• Employ statistical techniques to scrutinize data and produce actionable business insights.
• Collaborate with the management team to determine and rank the needs of different business units.
• Develop data dashboards, charts, and visual aids to support decision-making across departments.
• Convey insights through both reports and visual presentations.
• Partner with engineering and product development teams to understand business requirements.
• Engage with managers from various departments to specify data requirements for analysis projects tailored to their unique business processes.

• Programming Languages:
  Proficiency in languages for data manipulation, statistical analysis, and machine learning. SQL knowledge is crucial for extracting and querying data from databases.
• Statistical Analysis:
  A foundation in statistics is essential for interpreting data and conducting various analyses to extract meaningful insights.
• Data Visualization:
  Understanding tools like Tableau, Power BI, or even Python libraries (e.g., Matplotlib, Seaborn) to create compelling visual representations of data findings.
• Machine Learning:
  Understanding basic machine learning concepts and algorithms can be beneficial, especially for roles that overlap with data science.
• Software Proficiency:
  Familiarity with spreadsheet tools like Microsoft Excel for data entry, manipulation, and preliminary analysis. Experience with database management software and data analytics platforms is also valuable.
• Analytical Thinking:
  Think critically and analytically to solve problems and make data-driven decisions.
• Attention to Detail:
  Precision is key in data analysis to ensure data cleaning, analysis, and reporting accuracy.
• Communication Skills:
  Verbal and written communication skills are necessary to translate technical findings into understandable insights for non-technical stakeholders effectively.
• Problem-Solving:
  The capacity to approach complex data challenges with innovative solutions.
• Curiosity and Continuous Learning:
  A genuine interest in data and its implications for businesses, coupled with continuous learning and staying updated with industry trends and tools.
• Determine operational objectives by studying business functions, gathering information, and evaluating output requirements and formats
• Construct workflow charts and diagrams, studying system capabilities, and writing specifications
• Improve systems by studying current practices and designing modifications
• Recommend controls by identifying problems and developing improved procedures
• Define project requirements by managing project milestones, forming project teams, and establishing project budgets
• Monitor project progress by tracking activity, resolving problems, publishing progress reports, and recommending actions