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Georgia It, Inc. Interview Questions

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Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

Experiment Validity

Let’s say we have a table with an <code>id</code> and <code>name</code> fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.

Write a query to randomly sample a row from this table.

Input:

<code>big_table</code> table

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

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

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

Write a query to randomly sample a row from a big table.

Random SQL Sample

Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

Georgia It, Inc. Salaries by Position

Business Analyst Location: Bay Area, CA (Hybrid) Duration: Contract (12 Months) Rate: DOE US Citizens, GC visa holders preferred, NO third party corp to corp accepted for this job Job Description : Position Summary : We are seeking a Senior Business Analyst with a strong background in Contract Life Cycle Management (CLM) and Contract Decision Management (CDM) within the healthcare or pharmaceutical domain. The ideal candidate will have over 8-10 years of experience in the US healthcare sector and proven expertise in working with multi-vendor environments across different time zones. Palantir expertise is preferred but not mandatory. Key Responsibilities : <ul> <li> Lead the implementation and optimization of Contract Life Cycle Management (CLM) and Contract Decision Management (CDM) solutions in healthcare or pharma projects. </li> <li> Collaborate with stakeholders to gather and document business requirements, ensuring alignment with organizational goals and regulatory requirements. </li> <li> nalyze, design, and implement effective business processes and workflows related to contract management. </li> <li> Facilitate cross-functional team collaboration, including internal teams, external vendors, and stakeholders across multiple time zones. </li> <li> Provide subject matter expertise in the US healthcare domain, ensuring that solutions are compliant with industry standards and regulations. </li> <li> Conduct data analysis and reporting to support decision-making and continuous improvement initiatives. </li> <li> Utilize Palantir tools and technologies to enhance data integration, analysis, and visualization (preferred). </li> </ul> Qualifications : <ul> <li> Bachelor's degree in Information Technology, Healthcare Management or a related field. Master's degree preferred. </li> <li> Minimum of 8-10 years of experience as a Business Analyst, specifically within the US healthcare domain. </li> <li> Proven experience in Contract Life Cycle Management and Contract Decision Management implementation in healthcare or pharmaceutical settings. </li> <li> Strong understanding of US healthcare regulations, compliance, and industry standards. </li> <li> Experience working in a multi-vendor environment and managing cross-functional teams across different time zones. </li> <li> Proficiency in business analysis tools and methodologies. </li> <li> Excellent communication, interpersonal, and stakeholder management skills. </li> <li> Strong analytical and problem-solving abilities. </li> <li> Experience with Palantir technologies is highly desirable. </li> </ul> General Expectations : <ul> <li> bility to work independently and as part of a team in a fast-paced environment. </li> <li> Strong organizational skills and attention to detail. </li> <li> bility to handle multiple projects simultaneously and meet deadlines. </li> <li> Commitment to continuous learning and professional development. </li> <li> daptability and flexibility in responding to changing business needs and priorities. </li> </ul>

Role: Machine Learning Engineer/SRE Location: Chicago, IL or Remote Duration: 12 Months Rate: DOE US Citizens and Green cards & GC-EAD Only. No Third-party C2C available for this job Job Description: We are seeking a highly skilled and motivated Machine Learning Engineer who possesses expertise in developing, deploying, and managing machine learning models. In this role, you will be an integral part of our AI Engineering and Site Reliability Engineering (SRE) teams, responsible for managing Azure infrastructure for AI model development and deployment, monitoring and reporting model performance, and responding to outages/incidents related to model operations. Key Responsibilities: Manage Azure Infrastructure: Configure, maintain, and optimize Azure infrastructure for AI model development and deployment, ensuring scalability and performance. Model Performance Monitoring: Implement and maintain monitoring systems to track model performance, proactively identifying and addressing issues as they arise. Incident Response: Collaborate with the SRE team to respond promptly to outages and incidents related to model operations, ensuring minimal downtime and rapid issue resolution. Skills and Qualifications: Azure Infrastructure Experience: Proficiency in managing Azure infrastructure components, including virtual machines, storage, and networking, to support AI model development and deployment. CI/CD Pipeline Experience: Experience with Continuous Integration/Continuous Deployment (CI/CD) pipelines, including the automation of model deployment processes. Containerization in the Cloud: Strong knowledge of containerization technologies in the cloud, such as Docker and Kubernetes, for efficient deployment and scaling of machine learning models. Machine Learning Expertise: Proficient in building and optimizing machine learning models, with a deep understanding of various Client algorithms and frameworks. Programming Skills: Proficiency in programming languages commonly used in machine learning, such as Python and libraries like TensorFlow and PyTorch. Data Management: Experience in data preprocessing, feature engineering, and data pipeline development for machine learning. Collaborative Team Player: Excellent communication skills and the ability to work collaboratively with cross-functional teams, including AI engineers and SREs. Documentation: Effective documentation skills to maintain clear and organized records of models, infrastructure configurations, and incident responses. Preferred Qualifications : Experience with cloud-based machine learning platforms (e.g., Azure Machine Learning). Experience with CI/ CD tools to deploying Client services and applications specific to Azure cloud platform Familiarity with DevOps practices and tools for automating infrastructure and deployments. Knowledge of model versioning and model management tools. Understanding of security best practices in AI model deployment. Certifications in relevant areas, such as Azure certifications or machine learning certifications. Job titles of folks with these skills may vary - e.g. MLOps Lead, MLOps Solution/Delivery Architect or Senior Client Engineer

Position - Data Scientist Location - Houston, TX 1 Plus years contract Rate - DOE US Citizen, Green Card, GC EAD, and H4 EAD can apply for this role, No third party corp to corp. Must Have <ul> <li> Data scientist </li> <li> Data Analysis </li> <li> Data Visualization </li> <li> Git </li> <li> Linux/Unix Shell </li> <li> Machine Learning </li> <li> MLOps </li> <li> Python </li> <li> Software Engineering </li> <li> SQL </li> </ul> Nice to Haves <ul> <li> AWS </li> <li> AWS EC2 </li> <li> AWS S3 </li> <li> AWS Sagemaker </li> </ul> Responsibilities Work independently on data science / Client projects for multiple business functions Identify and frame the data science / Client opportunity from understanding the business problem / opportunity Gather, cleanse, and transform internal and external data Analyze data and deliver insights via visualizations and dashboards Create, productionize, and maintain models / solutions that address business problems Present, explain and defend results from analysis and modeling, and approach taken Participate in strategic planning discussions around optimizations, data science and big data analytics Qualifications: 5+ years of practical experience framing and solving business problems in supply chain, logistics, or operations with probability / statistics, visualization, inferential or predictive models 5+ years professional experience programming production code in Python Software engineering skills - best practices of software design, API design, knowledge of design patterns, version control, and production considerations such as performance, logging, maintainability, refactoring, testing and debugging Hands on experience with MLOps Experience building solutions in AWS is a plus Educational background in Statistics, Applied Mathematics, or scientific field is a plus Ability to adapt in a rapidly changing environment Ability to communicate insights and approaches in a simple, actionable manner Ability to work independently and with team members from different backgrounds Excellent attention to detail and problem-solving skills

Data Architect Location: Palo Alto, CA (Hybrid) Duration: Long Term Rate: DOE Key Responsibilities: Data Orchestration:<ul> <li>Design, implement, and manage data workflows using Airflow to automate and orchestrate data processing tasks.</li> <li>Optimize Airflow DAGs (Directed Acyclic Graphs) for performance and scalability.</li></ul>Task Management:<ul> <li>Develop and maintain distributed task processing using Celery and ensure robust task queue management with Redis or RabbitMQ.</li></ul>Database Management:<ul> <li>Design and manage databases using Cosmos DB, MongoDB, and PostgreSQL.</li> <li>Develop and maintain efficient data models and ensure data consistency and integrity.</li></ul>API and Webhooks:<ul> <li>Implement and manage FastAPI webhooks to handle data ingestion and integration tasks.</li> <li>Develop and maintain Azure Functions to support webhook operations and integrate with cloud services.</li></ul>Streaming Data:<ul> <li>Implement and manage Kafka Streams to handle real-time data processing and streaming requirements.</li></ul>Data Lake Management:<ul> <li>Work with Iceberg to manage and optimize large-scale data lake storage and querying.</li></ul>Collaboration and Communication:<ul> <li>Collaborate with data scientists, engineers, and business analysts to understand data requirements and provide technical solutions.</li> <li>Document processes, architectures, and configurations to ensure knowledge sharing and compliance with best practices.</li></ul>Required Skills and Qualifications: Experience and Knowledge:<ul> <li>Proven experience with Airflow for data orchestration and workflow management.</li> <li>Hands-on experience with Celery for task management and Redis or RabbitMQ for messaging.</li> <li>Proficiency with Cosmos DB, MongoDB, and PostgreSQL for data storage and management.</li> <li>Experience developing and managing webhooks using FastAPI and integrating with Azure Functions.</li> <li>Knowledge of Kafka Streams for real-time data processing.</li> <li>Familiarity with Iceberg for data lake management and optimization.</li> <li>Healthcare domain experience is good to have .</li></ul>Technical Skills:<ul> <li>Strong understanding of data pipelines, ETL processes, and data integration.</li> <li>Proficient in Python, with experience in building and maintaining data-oriented applications.</li> <li>Ability to work with large datasets and optimize performance across distributed systems</li></ul>Soft Skills:<ul> <li>Excellent problem-solving and analytical skills.</li> <li>Strong communication and collaboration skills.</li> <li>Ability to work independently and manage multiple priorities in a fast-paced environment.</li></ul>

Georgia It, Inc. Interview Questions

Georgia It, Inc. Interview Guides

Georgia It, Inc. Interview Questions

Georgia It, Inc. Salaries by Position

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