The SAIC Data Analyst interview guide, interview questions, salary data, and interview experiences.

SAIC Data Analyst Interview Guide

Getting ready for an Data Analyst interview at SAIC? The SAIC Data Analyst interview span across 10 to 12 different question topics. In preparing for the interview:
<ul>
<li>Know what skills are necessary for SAIC Data Analyst roles.</li>
<li>Gain insights into the Data Analyst interview process at SAIC.</li>
<li>Practice real SAIC Data Analyst interview questions.</li>
</ul>
Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the SAIC Data Analyst interview.


Getting ready for an Data Analyst interview at SAIC? The SAIC Data Analyst interview span across 10 to 12 different question topics. In preparing for the interview:

- Know what skills are necessary for SAIC Data Analyst roles.
- Gain insights into the Data Analyst interview process at SAIC.
- Practice real SAIC Data Analyst interview questions.

Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the SAIC Data Analyst interview.
        

Overview

SAIC Data Analyst Salary

We've gathered this data from parsing thousands of interview experiences sourced from members.

We've gathered this data from parsing thousands of interview experiences sourced from members.

Python

Machine Learning

Product Metrics

A/B Testing

Analytics

Statistics

Probability

Algorithms

SAIC Data Analyst Interview Process

As part of the financial management in a large corporation, the CFO wants to review the expenses in all departments for the previous financial year (2022).

Write a SQL query to calculate the total expenditure for each department in 2022. Additionally, for comparison purposes, return the average expense across all departments in 2022.

Note: The output should include the department name, the total expense, and the average expense (rounded to 2 decimal places). The data should be sorted in descending order by total expenditure.

<h3>Example:</h3>

Input:

<code>departments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>name</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<code>expenses</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>department_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>amount</td>
<td>FLOAT</td>
</tr>

<tr>
<td>date</td>
<td>DATE</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>department_name</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>total_expense</td>
<td>FLOAT</td>
</tr>

<tr>
<td>average_expense</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>

Calculate total and average expenses for each department.

Department Expenses

The probability that it will rain tomorrow is dependent on whether or not it is raining today and whether or not it rained yesterday.

If it rained yesterday and today, there is a 20% chance it will rain tomorrow. If it rained one of the days, there is a 60% chance it will rain tomorrow. If it rained neither today nor yesterday, there is a 20% chance it will rain tomorrow.

Given that it is raining today and that it rained yesterday, write a function <code>rain_days</code> to calculate the probability that it will rain on the nth day after today.

Example:

Input:

<pre tabindex="0" class="chroma"><code>n=5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def rain_days(n) -&gt; 0.39968
</code></pre>

Given that it is raining today and that it rained yesterday, write a function to calculate the probability that it will rain on the nth day after today.

Rain in N Days

What are time series models? Why do we need them when we have less complicated regression models?

What can happen if a traditional regression model is used on data that has autocorrelation?

What can happen if a traditional regression model is used on data that has autocorrelation?

Why Do We Need Time Series Models? I

Why can't standard regression models be used effectively for data points where current values are influenced by past values?

Why can't standard regression models be used effectively for data points where current values are influenced by past values?

Why Do We Need Time Series Models? II

Why Do We Need Time Series Models?

SAIC Data Analyst Interview Questions

Description SAIC has an immediate opening for a Chemical, Biological, Radiological, and Nuclear (CBRN) Defense Data Scientist supporting the Headquarters Air Force Deputy Chief of Staff for Strategic Deterrence and Nuclear Integration (AF/A10), Countering Weapons of Mass Destruction (CWMD) Division (AF/A10S), Operational Analysis Branch (AF/A10SA), on behalf of Air Force Installation Management Support Center (AFIMSC) and United States Air Force Europe-Air Force Africa (USAFE-AFAFRICA). Work will be performed onsite at the Pentagon or Joint Base Anacostia-Bolling. Responsibilities include:<ul><li>Serve as a CWMD (and nuclear effects when required) modeler to model complex systems which include CBRN agent effects and attributes in different situations (i.e., varying weather, release mechanisms and parameters) overlaid over differing major combat operations (MCO) related operational environments. </li><li>Extracting data from CBRN-related modeling programs through post processing utilities to provide analysts user-friendly data tables and graphics to depict complexities such as toxicity effects over time, degradation of agent strength, and contaminated area coverage as a function of time. </li><li>Develop and use batch programs that enable HAF/A10SA to complete thousands of CBRN-related hazard runs (HPAC, VLSTRACK, HOTSPOT, etc.) without having to execute each run on a one-by-one basis. </li><li>Use computer modeling to support technical analysis to address questions that arise in the CWMD operations (non-proliferation, targeting, interdiction, strike, counter-air/space actions) arena of USAFE-AFAFRICA ability to survive and operate (ATSO), and CBRN defense technical domains. </li><li>Use computer modeling to support technical research and operational evaluations to assess, interpret, shape, and help advise AF/A10S, on behalf of AFIMSC and USAFE-AFAFRICA, regarding what operational and scientific testing results mean to operators (e.g., pilot, navigator, aircrew, loadmasters, maintainers, defenders).</li></ul>Qualifications Required Skills: <ul><li>Top Secret clearance with the ability to obtain an SCI</li><li>Subject matter expert in computer programming for MCO-related modeling of CBRN materials (HPAC, VLSTRACK, HOTSPOT, etc.) and analyzing complex systems (Python, C++, Visual Basic, etc.). </li><li>Specialized expertise in providing modeling support for technical analysis of CBRN threats. </li><li>Excellent oral and written communication skills. </li><li>Proficiency with MS Office products. </li><li>Ability to work from alternate, off-site locations as required.</li></ul>Desired Experience/Skills: <ul><li>History of incorporating results of lab or field analysis of CBRN-related tests into modeling programs to support CBRN-related studies. </li><li>History of developing unique tools to assist analysts interpret and utilize raw data within analytical projects.</li></ul>TYPICAL EDUCATION AND EXPERIENCE:<ul><li>Bachelors and fourteen (14) years or more experience; Masters and twelve (12) years or more experience; PhD or JD and nine (9) years or more experience.</li></ul> Target salary range: $150,001 - $175,000. The estimate displayed represents the typical salary range for this position based on experience and other factors. SAIC accepts applications on an ongoing basis and there is no deadline. Covid Policy: SAIC does not require COVID-19 vaccinations or boosters. Customer site vaccination requirements must be followed when work is performed at a customer site.

Description SAIC is currently seeking a full time Data Scientist to join ourOmaha, NE team and begin an exciting and challenging career exploiting data science for environmental applications. RESPONSIBILITIES INCLUDE: •Responsible for supporting technique development efforts for applications within the federal government. •The ideal candidate is adept at using big data sets and applying machine learning and artificial intelligence techniques for analysis and prediction tools focused on environmental and meteorological applications. •S/He needs to have strong experience using a variety of data mining/data analysis methods, the use of a variety of data tools, constructing and imposing models, using/creating algorithms and creating/running simulations. •The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve enterprise outcomes. •Work with large, complex datasets and remedy difficult, non-routine analysis problems, applying advanced analytical methods as needed. •Conduct end-to-end analysis that consists of data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations. •Build and prototype analysis pipelines iteratively to provide insights at scale. •Develop comprehensive understanding of data structures and metrics, advocating for changes where needed for both products development and sales activity mainly with Azure based solutions. •Make recommendations such as cost-benefit, forecasting, and experiment analysis with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information. •Research and develop analysis, forecasting and optimization methods to improve the quality of operational data processing systems. •Interact cross-functionally with teams, and work closely with them to identify opportunities for design and to examine improvements for SAIC engaged solutions. •Apply data science concepts to build predictive analytics tools using cutting edged techniques to exploit big data •Bachelors and 5 years or more experience or 9 years of experience in lieu of degree •Experience with statistical software such as R, Python, MATLAB, pandas and database languages such as SQL •Experience with cloud platforms such as Azure, AWS or Google Cloud Platform •Desired skills: •Advanced degree in a quantitative discipline such as Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Mathematics, Physics, Atmospheric Science, Electrical Engineering Industrial Engineering. •8 years of directly relevant, tech industry work experience such as a statistician, or data scientist, with deep expertise in statistical data analysis, such as linear models, multivariate analysis, stochastic models, and sampling methods. •Applied experience with machine learning on large datasets. •Demonstrated skills in selecting the right statistical tools given a data analysis problem.: •Experience applying data science techniques to environmental science and/or weather-related challenges •Understanding of Numerical Weather Prediction principles •Experience/training in operational meteorology •Experience with Air Force weather forecasting systems •Must be a US Citizen •Must be able to obtain Secret Clearance Qualifications<ul><li>Bachelors and 5 years or more experience or 9 years of experience in lieu of degree </li><li>Experience with statistical software such as R, Python, MATLAB, pandas and database languages such as SQL</li><li>Experience with cloud platforms such as Azure, AWS or Google Cloud Platform</li><li>Desired skills:</li><li>Advanced degree in a quantitative discipline such as Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Mathematics, Physics, Atmospheric Science, Electrical Engineering Industrial Engineering.</li><li>8 years of directly relevant, tech industry work experience such as a statistician, or data scientist, with deep expertise in statistical data analysis, such as linear models, multivariate analysis, stochastic models, and sampling methods.</li><li>Applied experience with machine learning on large datasets.</li><li>Demonstrated skills in selecting the right statistical tools given a data analysis problem.:</li><li>Experience applying data science techniques to environmental science and/or weather-related challenges</li><li>Understanding of Numerical Weather Prediction principles</li><li>Experience/training in operational meteorology</li><li>Experience with Air Force weather forecasting systems</li></ul><ul><li>Must be a US Citizen</li><li>Must be able to obtain Secret Clearance </li></ul> SAIC accepts applications on an ongoing basis and there is no deadline. Covid Policy: SAIC does not require COVID-19 vaccinations or boosters. Customer site vaccination requirements must be followed when work is performed at a customer site.

Description SAIC is seeking an ATR Research Analyst who will delve into the critical examination of Automatic Target Recognition technology within automated collection systems.The analysis will determine the necessity and impact of ATR in enhancing the efficiency and accuracy of these systems. The ATR Research Analyst will be responsible for conducting a comprehensive study to assess whether ATR is an indispensable component in the automation loop, based on empirical findings and strategic assessments.It is expected that this study will be broken into phases as defined in the Key Responsibilities below, with the person/people in the position additionally responsible, as per the Research Framework Document deliverable, for developing a schedule for each phase of delivery, with final delivery expected by the end of current period of performance or any follow-on period of performances.To keep the research as unbiased as possible, it is expected that this position will be conducted from an offsite location. Key Responsibilities: •Research Design: Develop a robust research framework to evaluate the role and effectiveness of ATR in automated collection systems. •Data Analysis: Analyze current automated collection loops to understand the integration and performance of ATR technologies. •Comparative Studies: Compare systems with and without ATR capabilities to assess the impact on operational efficiency and intelligence gathering. •Strategic Assessment: Evaluate the strategic advantages and potential drawbacks of implementing ATR within various automated collection contexts. •Documentation: Prepare a detailed study report that outlines findings, conclusions, and recommendations regarding the necessity of ATR. •Presentation: Present the study's findings to stakeholders, providing actionable insights and guiding future technology investments. Qualifications -Experience in defense technology research, with a focus on automated systems and target recognition. - Strong analytical skills with the ability to interpret complex technical data. - Knowledge of current ATR technologies and their applications within automated collection systems. - Excellent written and verbal communication skills for reporting and presenting research findings. - A strategic thinker with the ability to assess long-term implications of technology integration. - Bachelor's degree with 14+ years relevant experience. - Active TS/SCI w/Poly Outcome: Your research will provide a decisive answer to the question of ATR's necessity in automated collection loops, influencing the direction of future technology development and deployment. Your findings will serve as a cornerstone for decision-making processes regarding the integration of ATR technology, potentially shaping the future of automated intelligence collection. Deliverables: •ATR Research Framework Document: Develop and submit a detailed ATR research framework document within the first month of appointment, outlining the methodologies and criteria for evaluating ATR technology. •Monthly Data Analysis Reports: Provide monthly reports on the analysis of current automated collection systems, focusing on the integration and performance of ATR technologies. •Comparative Study Papers: Complete at least two comparative study papers per year, contrasting systems with and without ATR capabilities, highlighting their impact on operational efficiency and intelligence gathering. •Strategic Assessment Reports: Produce biannual strategic assessment reports evaluating the advantages and potential drawbacks of ATR implementation in various contexts, providing a comprehensive view of its strategic impact. •Comprehensive Study Report: Prepare an in-depth study report annually, summarizing findings, conclusions, and recommendations on the necessity of ATR in automated collection systems. •Stakeholder Presentation Material: Develop and deliver presentation materials for stakeholder meetings, ensuring that the findings and insights are communicated effectively and can guide future technology investments. •Technology Integration Impact Analysis: Conduct and document an annual impact analysis of ATR technology integration, assessing its long-term implications on automated intelligence collection. •Feedback and Recommendations File: Maintain a file of feedback from stakeholders and operational teams, using it to refine future research and provide targeted recommendations on ATR technology deployment. SAIC accepts applications on an ongoing basis and there is no deadline. Covid Policy: SAIC does not require COVID-19 vaccinations or boosters. Customer site vaccination requirements must be followed when work is performed at a customer site.

SAIC Data Analyst Interview Guide

Overview

SAIC Data Analyst Salary

SAIC Data Analyst Interview Process

SAIC Data Analyst Interview Questions

SAIC Data Analyst Jobs