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Data Scientist Analyst I

Job Summary:Be a part of the IT Applications team and provide expertise on data analysis techniques using software tools, while working with business stakeholders to define requirements and deploy solutions on the enterprise data lake.Job Responsibilities:• Analyze business requirements and design/modify data model• Support critical production environments for Confluent Kafka, MongoDB• Develop queries and reports from existing databases for reporting requirements• Documenting architectures for new or existing solutions• Create AI/ML solutions using cutting-edge cloud technologies like AWS BedrockRequired Qualifications:• Bachelor's degree (preferably in Computer Science or similar)• 0-3 years of experiencePreferred Qualifications:• Familiarity with programming languages and databases (C, C++, Java, Python, Oracle, MySQL, SQL)• Knowledge of data modeling techniques• Familiarity with structured and unstructured data• Deploying/Running/troubleshooting in a production environment• Familiarity with databases and cloud technologies (such as AWS RDS, AWS Redshift, MongoDB, AWS Quicksite)Full-Time Equivalent Annual Salary Range: $55,300 - $110,600 for an Individual Contributor I at Remote USA. The disclosed salary range is a general guideline for Mercury team members employed at the job level at the location listed above and does not include overtime for eligible hourly employees."This position requires you to access information that is subject to U.S. export regulations. You may only access such information if you are a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. government."Why should you join Mercury Systems?Mercury Systems is a technology company that makes the world a safer, more secure place. We push processing power to the tactical edge, making the latest commercial technologies profoundly more accessible for today's most challenging aerospace and defense missions. From silicon to system scale, Mercury enables customers to accelerate innovation and turn data into decision superiority. Headquartered in Andover, Massachusetts, Mercury employs more than 2,300 people in 24 locations worldwide. To learn more, visit mrcy.comOur CultureWe are committed to making Mercury a great place to work, no matter where our employees are located. We offer a casual and enjoyable atmosphere that allows employees to learn and grow. We help and care for one another and work as one to achieve results for us and for our customers. We value communication and transparency, and strive to foster two-way dialogue at all levels of the organization. We are committed to lifelong learning, offering comprehensive skills training and tuition reimbursement. Whether you're just starting out on your career journey or you are an experienced professional, it's important to us that you feel recognized and rewarded for your contributions.To find out more about Why Mercury?, or visit the Mercury Community or find answers to general questions at Mercury FAQsMercury Systems is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex (including pregnancy), sexual orientation, gender identity, national origin, genetic information, creed, citizenship, disability, protected veteran or marital status.As an equal opportunity employer, Mercury Systems is committed to a diverse workforce. In order to ensure reasonable accommodation for individuals protected by Section 503 of the Rehabilitation Act of 1973, the Vietnam Veterans' Readjustment Act of 1974, and Title I of the Americans with Disabilities Act of 1990. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact the number below.Click here read about our recent press release.Click Here to learn about OFCCP

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What You Should Know About Data Scientist Analyst I, Mercury Systems

Are you ready to dive into the world of data? Mercury Systems is on the lookout for a Data Scientist Analyst I to join our dynamic IT Applications team located in Marriottsville, MD! This exciting role allows you to leverage your expertise in data analysis techniques while collaborating with business stakeholders to understand their needs. You'll be instrumental in analyzing business requirements and modifying data models to ensure efficient data flow. With your skills, you’ll support critical production environments such as Confluent Kafka and MongoDB, developing queries and reports to deliver key insights. Additionally, you'll document architectures for both new and existing solutions, ensuring smooth operations. If you’re passionate about creating AI and ML solutions using cutting-edge cloud technologies, such as AWS Bedrock, then this position is a fantastic fit for you. The ideal candidate will have a Bachelor's degree in Computer Science or a related field, along with 0-3 years of experience. Familiarity with programming languages like Python, as well as database technologies, is preferred. At Mercury, we pride ourselves on fostering a positive work culture, dedicated to lifelong learning and employee growth. Join us in making the world a safer, more secure place through your contributions in the tech industry. Let’s embark on this journey together!

Frequently Asked Questions (FAQs) for Data Scientist Analyst I Role at Mercury Systems
What are the responsibilities of a Data Scientist Analyst I at Mercury Systems?

As a Data Scientist Analyst I at Mercury Systems, you will analyze business requirements and modify data models accordingly. You will work on supporting production environments utilizing Confluent Kafka and MongoDB, developing various queries and reports from existing databases. The role also involves documenting architectures for new or existing solutions and creating AI/ML applications with cloud technologies like AWS Bedrock.

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What qualifications do I need to become a Data Scientist Analyst I at Mercury Systems?

To become a Data Scientist Analyst I at Mercury Systems, you should hold a Bachelor's degree, preferably in Computer Science or a related field. While 0-3 years of experience is sufficient, familiarity with programming languages and databases, like C, Java, Python, and SQL, along with knowledge in data modeling techniques, is highly preferred.

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What programming languages are beneficial for a Data Scientist Analyst I at Mercury Systems?

A Data Scientist Analyst I at Mercury Systems can benefit from knowledge in programming languages such as Python, C, C++, and Java. Additionally, proficiency in databases like Oracle, MySQL, and SQL is advantageous. Understanding how to work with both structured and unstructured data is also crucial for this position.

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Can I work remotely as a Data Scientist Analyst I at Mercury Systems?

Yes! The position of Data Scientist Analyst I at Mercury Systems offers the possibility of remote work. This flexibility allows you to manage your work-life balance while contributing effectively to the team from the comfort of your chosen workspace.

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What kind of projects will I be working on as a Data Scientist Analyst I at Mercury Systems?

In the role of Data Scientist Analyst I at Mercury Systems, you can expect to work on various projects involving data analysis, development of reports, and creating AI/ML solutions. Collaborating with other IT professionals and business stakeholders, you'll directly impact the company’s ability to utilize data efficiently and innovate in the tech space.

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Common Interview Questions for Data Scientist Analyst I
Can you describe your experience with data analysis tools?

When responding to this question, highlight your proficiency with specific tools you have used, such as SQL, Python, or specialized data analysis software. Discuss a project where you effectively utilized these tools to analyze datasets and draw actionable insights.

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Explain a data modeling technique you have used on past projects.

To answer effectively, illustrate a specific technique you employed, such as Entity-Relationship modeling or dimensional modeling. Provide context on how you applied it, the challenges faced, and the positive outcomes achieved for your previous projects.

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How comfortable are you with cloud technologies like AWS?

In your response, indicate your level of familiarity with AWS services, highlighting any specific projects where you utilized AWS for data storage, processing, or analytics. Discuss how cloud technologies have enhanced your workflow and data management.

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What strategies do you use for troubleshooting data discrepancies?

When addressing this question, discuss your systematic approach to identify issues, including validating data sources, employing debugging techniques, and collaborating with stakeholders for resolution. Describe a specific instance where you successfully resolved data issues.

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Describe your experience working with both structured and unstructured data.

Provide an overview of your experience with structured data (like databases) and unstructured data (like social media data). Mention specific tools or techniques you have used to handle both dataset types and the insights you derived from them.

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How do you prioritize tasks when managing multiple projects?

Discuss your methods for prioritization, whether through tools like Kanban boards, and Agile methodologies or simply based on project deadlines and importance. Share an example of how you successfully managed competing deadlines in past roles.

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What is your experience with developing AI/ML solutions?

Highlight relevant experiences where you’ve developed AI/ML models or solutions. Discuss specific technologies, data types used, and any successes or lessons learned. If applicable, share the impact of these solutions on business outcomes.

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How would you explain complex data concepts to a non-technical audience?

Your answer should demonstrate your communication skills. Share how you break down complex jargon into simple language using analogies or visual aids, ensuring that the audience understands the significance of the data insights.

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What challenges have you faced working in a production environment?

Reflect on specific challenges like downtime issues, data inaccuracies, or system integration hurdles. Discuss how you managed those challenges and what strategies you implemented to ensure smooth operations in the production environment.

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How do you stay updated on the latest trends in data science?

Mention resources you use, such as industry publications, online courses, seminars, or professional networks. Highlight your commitment to lifelong learning and how you actively seek opportunities to expand your knowledge in the field.

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At Mercury, our mission is to drive superlative results for our customers by developing innovative, rugged open processing subsystems that leverage the best commercial-item technology available to solve complex defense electronics problems.  Mercu...

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Full-time, remote
DATE POSTED
December 22, 2024

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