Data Scientist (3-5 years experience)

As a Data Scientist at DataLab USA™, you’ll be deeply involved in building, implementing, and maintaining predictive models. Your role involves identifying patterns and trends in data to aid business decisions, monitoring monthly model recalibration processes, and generating complex ad-hoc analyses. You'll also be tasked with designing new analytic procedures and reviewing data quality for corporate model scoring.

To qualify for the Data Scientist role at DataLab USA™, you typically need a Master’s or PhD in a quantitative discipline. Alternatively, a Bachelor’s degree with significant experience can be acceptable. Additional qualifications include regular use of SQL, proficiency in programming languages (like R or Python), experience with data visualization tools such as Tableau, and familiarity with Excel.

DataLab USA™ prefers candidates with 3-5 years of experience in database marketing and data mining. Additionally, experience in the financial or insurance industry is strongly preferred. This experience enables you to understand client strategies better and provide actionable insights.

DataLab USA™ offers a comprehensive benefits package for its Data Scientist employees, which includes health, dental, and vision plans, an Employee Assistance Program (EAP), a 401(k) retirement savings plan, paid time off for vacations and sick leave, and nine paid company holidays throughout the year.

Unfortunately, the Data Scientist position at DataLab USA™ is not available for remote work. Candidates must be local to Germantown, MD, as collaboration and synergy with the team are key components of success in this role.

When discussing your predictive modeling experience, focus on specific projects where you've developed models from start to finish. Talk about the tools and methodologies used, how you identified patterns in the data, and the impact those models had on business decisions.

Explain your systematic approach to data cleaning, emphasizing the importance of accurate data. Mention specific techniques like handling missing values, data normalization, or any tools you prefer using for this purpose.

Highlight the programming languages you are comfortable with, such as R or Python. Discuss specific projects where you utilized these languages, detailing the outcomes and how they helped in achieving the objectives.

Detail your strategies for ensuring accuracy, like conducting regular quality checks, recalibrating models based on new data, and validating models with test datasets. Provide an example where you successfully improved a model's accuracy.

Share a specific case of a complex analysis you’ve performed, focusing on the methods used and the insights gained. Highlight how those insights influenced decision-making at your organization.

Discuss your frameworks for project prioritization, such as considering business impact, resource availability, and urgency. Provide examples that reflect your ability to manage expectations effectively.

Mention specific tools, like Tableau or Power BI, that you've used to visualize data. Emphasize the importance of data visualization in making complex data comprehensible and assisting stakeholders in decision-making.

Share your strategies for continuous learning, such as subscribing to data science journals, attending webinars, or participating in relevant online courses. Illustrate how staying current has positively impacted your work.

Discuss your experience working in teams and how collaboration enhances project outcomes. Share examples of successful collaborations with other departments and how collective efforts led to achieving goals.

Outline your regular use of SQL for querying data. Share specific instances when your SQL queries enabled you to extract valuable insights, demonstrating your capability to leverage databases effectively.