The Ieee Business Intelligence interview guide, interview questions, salary data, and interview experiences.

Ieee Business Intelligence Interview Guide

Getting ready for an Business Intelligence interview at Ieee? The Ieee Business Intelligence interview span across 10 to 12 different question topics. In preparing for the interview:
<ul>
<li>Know what skills are necessary for Ieee Business Intelligence roles.</li>
<li>Gain insights into the Business Intelligence interview process at Ieee.</li>
<li>Practice real Ieee Business Intelligence 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 Ieee Business Intelligence interview.


Getting ready for an Business Intelligence interview at Ieee? The Ieee Business Intelligence interview span across 10 to 12 different question topics. In preparing for the interview:

- Know what skills are necessary for Ieee Business Intelligence roles.
- Gain insights into the Business Intelligence interview process at Ieee.
- Practice real Ieee Business Intelligence 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 Ieee Business Intelligence interview.
        

Overview

Ieee Business Intelligence 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.

Ieee Business Intelligence Interview Process

We’re given a table of bank transactions with three columns, <code>user_id</code>, a deposit or withdrawal value (determined if the value is positive or negative), and <code>created_at</code> time for each transaction.

Write a query to get the total three-day rolling average for deposits by day.

Note: Please use the format <code>&#39;%Y-%m-%d&#39;</code> for the date in the outout

Example:

Input:

<code>bank_transactions</code> table

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

<tbody>
<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>transaction_value</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td><code>dt</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>rolling_three_day</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>

Rolling Bank Transactions

You’ve been asked to analyze the player’s behaviors at a gaming company.

Write a query to get the number of players who played between 5 and 10 games (5 and 10 excluded), and the number of players who played 10 games or more.

Example:

Input:

<code>players</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>

<tr>
<td>games_played</td>
<td>INTEGER</td>
</tr>

<tr>
<td>registered_at</td>
<td>TIMESTAMP</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td>players_more_than_5_to_10_games</td>
<td>INTEGER</td>
</tr>

<tr>
<td>players_10_plus_games</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Obtain count of players based on games played.

Player Analysis

What are the logistic and softmax functions? What is the difference between the two?

What makes them useful for use in logistic regression?

In logistic regression, how is the predicted class determined based on the logistic function?

In logistic regression, how is the predicted class determined based on the logistic function?

Softmax vs Logistic II

What is the key characteristic of the logistic function that makes it useful for mapping continuous values to a probability?

What is the key characteristic of the logistic function that makes it useful for mapping continuous values to a probability?

Softmax vs Logistic I

What are the logistic and softmax functions? What is the difference between the two?

Softmax vs Logistic

Ieee Business Intelligence Interview Questions

Job Summary
The overall purpose of this position is to develop and promote an Advanced Analytics practice using Artificial Intelligence (AI) and Machine Learning (ML) within Information Technology. This is a hands-on position and the incumbent in this position is primarily responsible for development, design and implementation of advanced analytics solutions using AI & ML techniques, with the goal of gaining insights into all aspects of IEEE's business, be it customer/member acquisition/engagement/retention, geo-unit activities/participation, volunteer onboarding/initiatives/participation/continuity, conference logistics/statistics/performance, publication production/consumption/profitability, standards opportunity/development/adoption, industry engagement etc., to advance IEEE's mission of fostering technological innovation and excellence for the benefit of humanity.
The role will report to the Director, Chief Enterprise & AI Solutions Architect, and will not manage any direct reports. The individual in this position is expected to work independently under minimum direction and supervision.

Key Responsibilities

Designs and builds leading edge predictive models and AI/ML algorithms for initial simulations or prototypes and subsequent ingestion into mainstream software applications.

Designs and develops data collection processes to support these models.

Design A/B testing framework for product-driven decisions.

Builds a structure and process for modeling, predicting, and classifying user behavior and other business metrics.

Studies, explores, and evaluates new and existing data sources to determine their effectiveness and accuracy for use in AI/ML models. Provide actionable recommendations to business stakeholders.

Works with technology stakeholders to define the big data infrastructure needed to design and develop the AI/ML models.

Assists in growing the Data Science discipline in IEEE and applying Artificial Intelligence techniques (including Data Science and Machine Learning) where appropriate in delivering technology solutions to business problems.

Helps evangelize the use of AI/ML solutioning by showcasing the outcomes to business stakeholders.

Develops tools and processes to periodically validate AI/ML models against expected and observed outcomes and assess impact on business defined key performance indicators; Iteratively improve models based on findings.

Contributes to the development of architectural designs for data acquisition by and the delivery of AI-driven insights to the systems of engagement.

Implements new technologies like generative AI and large language models.

Contributes to the organizational adoption of generative AI and the establishment of a governance model for generative AI applications.

Qualifications

Education

Bachelor's degree or equivalent experience in Computer Applications/Computer Science/Computer Engineering/Applied Mathematics/Statistics/related fields or equivalent experience (Required).

Master's or other advanced degree in Computer Applications/Computer Science/Computer Engineering/Applied Mathematics/Statistics (Preferred).

Work Experience

7-10 years relevant experience in Information Technology (Preferred).

3+ years of hands-on experience in developing AI and ML solutions (Required).

4+ years of hands-on experience using statistical computer languages, such as R or Python (Required).

2+ years of experience working with data science tools, such as Pandas, NumPy, MatLab or Jupyter Notebooks (Required).

3+ years in developing Advanced Analytics solutions, including but not limited to, Data Visualization tools (Required).

5+ years hands-on experience in SQL and database technologies (Required).

2+ years of experience working with cloud services, preferably AWS (Required).

4-7 years experience in IT/Software/Application development and design (Required).

2+ years of experience working with large data sets and using big data solutions such as Hadoop, Hive, Spark, MongoDB and Redshift (Preferred).

Skills and Requirements

Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.

Rigorous understanding of statistics and ability to discern appropriate statistical techniques to solve problems.

Proven ability to initiate and drive projects from conception to completion given poorly scoped problem statements.

Proven analytical thinking and problem-solving skills.

Strong experience using data mining/data analysis methods, a variety of data tools, building and implementing AI/ML models, using/creating algorithms and creating/running simulations using both structured and unstructured data sets.

Experience in evaluating software tools & techniques and making recommendations.

Proven record of excellent analytical skills and understanding business needs.

Excellent communication and consensus-building abilities.

Other Requirements:
As defined in IEEE Policies, individuals currently serving on an IEEE board or committee are not eligible to apply.
PLEASE NOTE: This position is not budgeted for employer-sponsored immigration support, this includes all persons in F (both CPT and OPT), J, H, L, or O status.
For information on work demands and conditions required for this position, please consult the reference document, "Physical, Mental, and Work Environment Standards for IEEE Positions." This position is classified under Category I - Office Positions.
IEEE is an EEO/AAP Employer/Protected Veteran/Disabled.

Job: Technology

Primary Location: United States-New Jersey-Piscataway

Schedule: Full-time

Job Type: Regular

Job Posting: Nov 5, 2024, 2:48:26 PM

#J-18808-Ljbffr

Job Summary The overall purpose of this position is to develop and promote an Advanced Analytics practice using Artificial Intelligence (AI) and Machine Learning (ML) within Information Technology. This is a hands-on position and the incumbent in this position is primarily responsible for development, design and implementation of advanced analytics solutions using AI & ML techniques, with the goal of gaining insights into all aspects of IEEE's business, be it customer/member acquisition/engagement/retention, geo-unit activities/participation, volunteer onboarding/initiatives/participation/continuity, conference logistics/statistics/performance, publication production/consumption/profitability, standards opportunity/development/adoption, industry engagement etc., to advance IEEE's mission of fostering technological innovation and excellence for the benefit of humanity. The role will report to the Director, Chief Enterprise & AI Solutions Architect, and will not manage any direct reports. The individual in this position is expected to work independently under minimum direction and supervision. Key Responsibilities <ul><li>Designs and builds leading edge predictive models and AI/ML algorithms for initial simulations or prototypes and subsequent ingestion into mainstream software applications. </li><li>Designs and develops data collection processes to support these models. </li><li>Builds a structure and process for modeling, predicting, and classifying user behavior and other business metrics. </li><li>Studies, explores, and evaluates new and existing data sources to determine their effectiveness and accuracy for use in AI/ML models. Provides actionable recommendations to business stakeholders. </li><li>Works with technology stakeholders to define the big data infrastructure needed to design and develop the AI/ML models. </li><li>Assists in growing the Data Science discipline in IEEE and applying Artificial Intelligence techniques (including Data Science and Machine Learning) where appropriate in delivering technology solutions to business problems. </li><li>Helps evangelize the use of AI/ML solutioning by showcasing the outcomes to business stakeholders. </li><li>Develops tools and processes to periodically validate AI/ML models against expected and observed outcomes and assess impact on business defined key performance indicators; Iteratively improve models based on findings. </li><li>Contributes to the development of architectural designs for data acquisition by and the delivery of AI-driven insights to the systems of engagement. </li><li>Implements new technologies like generative AI and large language models. </li><li>Contributes to the organizational adoption of generative AI and the establishment of a governance model for generative AI applications. </li> </ul>Qualifications Education <ul><li>Bachelor's degree or equivalent experience in Computer Applications/ Computer Science/ Computer Engineering/Applied Mathematics/ Statistics or related fields. </li><li>Master's or other advanced degree in Computer Applications/ Computer Science/ Computer Engineering/Applied Mathematics/ Statistics preferred. </li> </ul>Work Experience <ul><li>7-10 years relevant experience in Information Technology preferred. </li><li>3+ years of hands-on experience in developing AI and ML solutions required. </li><li>4+ years of hands-on experience using statistical computer languages, such as R or Python required. </li><li>2+ years of experience working with data science tools, such as Pandas, NumPy, MatLab or Jupyter Notebooks required. </li><li>3+ years in developing Advanced Analytics solutions, including but not limited to, Data Visualization tools required. </li><li>5+ years hands-on experience in SQL and database technologies required. </li><li>2+ years of experience working with cloud services, preferably AWS required. </li><li>4-7 years experience in IT/Software/Application development and design required. </li><li>2+ years of experience working with large data sets and using big data solutions such as Hadoop, Hive, Spark, MongoDB and Redshift preferred. </li> </ul>Skills and Requirements <ul><li>Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. </li><li>Rigorous understanding of statistics and ability to discern appropriate statistical techniques to solve problems. </li><li>Proven ability to initiate and drive projects from conception to completion given poorly scoped problem statements. </li><li>Proven analytical thinking and problem solving skills. </li><li>Strong experience using data mining/data analysis methods, a variety of data tools, building and implementing AI/ML models, using/creating algorithms and creating/running simulations using both structured and unstructured data sets. </li><li>Experience in evaluating software tools & techniques and making recommendations. </li><li>Proven record of excellent analytical skills and understanding business needs. </li><li>Excellent communication and consensus building abilities. </li> </ul>Other Requirements As defined in IEEE Policies, individuals currently serving on an IEEE board or committee are not eligible to apply. PLEASE NOTE: This position is not budgeted for employer-sponsored immigration support, this includes all persons in F (both CPT and OPT), J, H, L, or O status. For information on work demands and conditions required for this position, please consult the reference document, "Physical, Mental, and Work Environment Standards for IEEE Positions." This position is classified under Category I - Office Positions. A non-profit organization, IEEE is the world's leading professional association for the advancement of technology. #J-18808-Ljbffr

Description Job Summary The overall purpose of this position is to develop and promote an Advanced Analytics practice using Artificial Intelligence (AI) and Machine Learning (ML) within Information Technology. This is a hands-on position and the incumbent in this position is primarily responsible for development, design and implementation of advanced analytics solutions using AI & ML techniques, with the goal of gaining insights into all aspects of IEEE's business, be it customer/member acquisition/engagement/retention, geo-unit activities/participation, volunteer onboarding/initiatives/participation/continuity, conference logistics/statistics/performance, publication production/consumption/profitability, standards opportunity/development/adoption, industry engagement etc., to advance IEEE's mission of fostering technological innovation and excellence for the benefit of humanity. The role will report to the Director, Chief Enterprise & AI Solutions Architect, and will not manage any direct reports. The individual in this position is expected to work independently under minimum direction and supervision. Key Responsibilities <ul> <li>Designs and builds leading edge predictive models and AI/ML algorithms for initial simulations or prototypes and subsequent ingestion into mainstream software applications</li> <li>Designs and develops data collection processes to support these models</li> <li>Design A/B testing framework for product-driven decisions</li> <li>Builds a structure and process for modeling, predicting, and classifying user behavior and other business metrics</li> <li>Studies, explores, and evaluates new and existing data sources to determine their effectiveness and accuracy for use in AI/ML models. Provide actionable recommendations to business stakeholders</li> <li>Works with technology stakeholders to define the big data infrastructure needed to design and develop the AI/ML models</li> <li>Assists in growing the Data Science discipline in IEEE and applying Artificial Intelligence techniques (including Data Science and Machine Learning) where appropriate in delivering technology solutions to business problems</li> <li>Helps evangelize the use of AI/ML solutioning by showcasing the outcomes to business stakeholders</li> <li>Develops tools and processes to periodically validate AI/ML models against expected and observed outcomes and assess impact on business defined key performance indicators; Iteratively improve models based on findings</li> <li>Contributes to the development of architectural designs for data acquisition by and the delivery of AI-driven insights to the systems of engagement</li> <li>Implements new technologies like generative AI and large language models</li> <li>Contributes to the organizational adoption of generative AI and the establishment of a governance model for generative AI applications</li> </ul>Qualifications Education <ul><li>Bachelor's degree or equivalent experience Computer Applications/ Computer Science/ Computer Engineering/Applied Mathematics/ Statistics/ related fields or equivalent experience Req </li> <li>Master's or other advanced degree Computer Applications/ Computer Science/ Computer Engineering/Applied Mathematics/ Statistics Pref </li> </ul>Work Experience <ul><li>7-10 years relevant experience in Information Technology Pref </li> <li>3+ years of hands-on experience in developing AI and ML solutions Req </li> <li>4+ years of hands-on experience using statistical computer languages, such as R or Python Req </li> <li>2+ years of experience working with data science tools, such as Pandas, NumPy, MatLab or Jupyter Notebooks Req </li> <li>3+ years in developing Advanced Analytics solutions, including but not limited to, Data Visualization tools. Req </li> <li>5+ years hands on experience in SQL and database technologies. Req </li> <li>2+ years of experience working with cloud services, preferably AWS Req </li> <li>4-7 years years experience IT/Software/Application development and design Req </li> <li>2+ years of experience working with large data sets and using big data solutions such as Hadoop, Hive, Spark, MongoDB and Redshift Pref </li> </ul>Skills and Requirements <ul><li>Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc </li> <li>Rigorous understanding of statistics and ability to discern appropriate statistical techniques to solve problems </li> <li>Proven ability to initiate and drive projects from conception to completion given poorly scoped problem statements. </li> <li>Proven analytical thinking and problem solving skills. </li> <li>Strong experience using data mining/data analysis methods, a variety of data tools, building and implementing AI/ML models, using/creating algorithms and creating/running simulations using both structured and unstructured data sets </li> <li>Experience in evaluating software tools & techniques and making recommendations </li> <li>Proven record of excellent analytical skills and understanding business needs </li> <li>Excellent communication and consensus building abilities </li> </ul>Other Requirements: As defined in IEEE Policies, individuals currently serving on an IEEE board or committee are not eligible to apply. PLEASE NOTE: This position is not budgeted for employer-sponsored immigration support, this includes all persons in F (both CPT and OPT), J, H, L, or O status. For information on work demands and conditions required for this position, please consult the reference document, "Physical, Mental, and Work Environment Standards for IEEE Positions." This position is classified under Category I - Office Positions. IEEE is an EEO/AAP Employer/Protected Veteran/Disabled Disclaimer: This job description is proprietary to IEEE. It outlines the general nature and key features performed by various positions that share the same job classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties and qualifications required of all employees assigned to the job. Nothing in this job description restricts management's right to assign or re-assign duties to this job at any time due to reasonable accommodations or other business reasons.

Ieee Business Intelligence Interview Guide

Overview

Ieee Business Intelligence Salary

Ieee Business Intelligence Interview Process

Ieee Business Intelligence Interview Questions

Ieee Business Intelligence Jobs