Data Scientist - W2 basis only

As a Data Scientist with us, you'll be responsible for analyzing complex datasets to derive actionable insights, developing predictive models using machine learning techniques, and presenting your findings in a compelling way to stakeholders. Your work will directly influence decision-making in our organization.

To qualify for the Data Scientist position in Atlanta, we require a Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field. Additionally, candidates should have over 8 years of experience in data science or analytics and proficiency in programming languages such as Python or R, alongside strong skills in SQL.

We believe in nurturing our talent, which is why we offer various opportunities for professional development. As a Data Scientist, you will have access to workshops, conferences, and may even engage in collaborative projects that align well with your career aspirations.

In this role, data scientists utilize a variety of tools including Python or R for programming, SQL for database management, as well as machine learning libraries like scikit-learn and TensorFlow. Visualization tools like Tableau and Power BI are also part of the daily toolkit for presenting findings.

Our team culture in Atlanta fosters collaboration and innovation, encouraging all Data Scientists to share insights and work together on challenging problems. We are passionate about data and pride ourselves on creating an inclusive and supportive environment.

During the interview, focus on specific machine learning projects you've worked on, detailing the libraries you used, such as TensorFlow or scikit-learn. Discuss your process for model selection, training, and evaluation, as well as any challenges you encountered along the way.

Highlight your methodical approach to data cleaning, including identifying missing data, outliers, and ensuring data quality. Discuss specific tools or techniques you've used to clean datasets and make them ready for analysis.

Identify key performance indicators relevant to your past experiences and explain their importance. This could include accuracy, precision, recall in machine learning models, and how you utilize these metrics to make data-driven decisions.

Share a detailed account of a specific project, including the objectives, methods used, and the outcomes. Focus on your role, the statistical techniques employed, and any software tools that were instrumental in achieving project goals.

Discuss strategies you employ, such as using data visualization tools for clarity, simplifying technical jargon, and tailoring your communication style to your audience to ensure they grasp the essential insights from your analysis.

Emphasize how you utilize Python or R for data manipulation, analysis, and building machine learning models. Discuss specific libraries or packages you frequently use and how they enhance your workflow.

Provide a compelling narrative of a past analysis, detailing how your findings led to actionable insights that drove significant business changes. Highlight the situation, your analysis and the results on business strategy.

Discuss common challenges such as data accessibility, ensuring data quality, or keeping up with the rapidly evolving technology landscape. Illustrate how you tackle these challenges with specific examples from your experience.

Articulate your approach to continuous learning, mentioning any relevant books, online courses, webinars, or communities you engage with. This demonstrates your commitment to staying informed and relevant in the field of data science.

Outline your familiarity with advanced techniques such as deep learning, reinforcement learning, or ensemble methods. Discuss any specific projects where you've implemented these techniques and their impact on your data analysis efforts.