Data Scientist / Architect

As a Data Scientist / Architect at our company, your main responsibilities include designing and maintaining robust data models tailored for complex analytic and operational requirements. You'll engineer scalable data solutions utilizing languages like ECL, SQL, Python/Spark, or Java and collaborate closely with mission stakeholders to assess and fulfill their data needs. Additionally, you'll be tasked with analyzing structured datasets to enhance usability and ensuring compliance with mission requirements through effective data governance.

To qualify for the Data Scientist / Architect position, candidates should have a minimum of 5 years of experience in data modeling, architecture, or data science. Proficiency in programming languages such as ECL, SQL, Python/Spark, or Java is crucial. A Bachelor of Science degree in a related field or equivalent professional experience is expected, along with excellent communication skills to bridge technical concepts with non-technical audiences.

A successful Data Scientist / Architect should be skilled in at least one or more programming languages such as ECL, SQL, Python/Spark, or Java. Familiarity with these languages is essential for engineering scalable data solutions and analyzing structured datasets effectively, allowing the transformation of raw data into usable intelligence that drives mission success.

The salary for the Data Scientist / Architect position varies based on factors like location and experience, generally ranging between $225,000 and $250,000. This competitive compensation reflects the skills and contributions expected from candidates who are instrumental in delivering high-value services.

Currently, the Data Scientist / Architect role does not specify a location, meaning there may be potential for remote work. However, requirements regarding active security clearances like TS/SCI with Full Scope Polygraph may impact the possibility of remote arrangements, particularly for specific missions that require in-office collaboration.

When answering this question, focus on specific projects where you developed data models. Highlight the techniques and tools you used, the challenges you faced, and how your contributions led to successful outcomes. It’s essential to illustrate your deep understanding of how data models support business requirements.

Discuss your proficiency with languages such as ECL, SQL, Python/Spark, or Java. Elaborate on how you choose the appropriate language based on the project's requirements, your personal strengths, and the nature of the data being handled. Providing examples of past work where you utilized these languages effectively will strengthen your response.

Emphasize systematic approaches to data quality, including validation techniques, regular audits, and feedback loops with users. Discuss specific measures you've implemented to maintain data integrity and ensure that all stakeholders have access to accurate data for decision-making.

Share a relevant situation where you engaged with stakeholders to gather requirements or provide insights. Highlight your communication skills and how you translated technical data challenges into accessible concepts for a non-technical audience, leading to successful project outcomes.

Identify common issues like data inconsistencies or integration problems with diverse sources. Discuss strategies you employed to address these challenges, demonstrating your problem-solving skills and an understanding of the importance of data harmonization.

Talk about your approach to breaking down datasets into manageable segments and utilizing data processing techniques or tools. Mention any methodologies or frameworks you follow that help you analyze data critically and identify key insights swiftly.

Respond by detailing your understanding of data governance principles. Share your experience in developing frameworks for data quality assurance, along with your collaboration with teams to enforce best practices that ensure data compliance and security.

Discuss methods you employ to remain informed, such as attending workshops, enrolling in courses, or following industry leaders online. Highlight your commitment to continuous learning and how this knowledge impacts your work positively.

Explain techniques you use for project management and prioritization, mentioning tools or frameworks that assist you in staying organized. Share how you assess project urgency and importance to effectively deliver results while managing time.

Highlight that clear communication is crucial for translating complex data concepts into digestible insights for diverse audiences. Mention how you utilize both verbal and written communication to ensure alignment with stakeholders and promote collaboration across teams.