Senior Data Scientist - job 1 of 2

As a Senior Data Scientist at FIS Global, your key responsibilities include developing machine learning models, creating large-scale data pipelines, and providing actionable insights by analyzing data for trends and anomalies. You will work closely with cross-functional teams and clearly communicate your findings to help shape product strategies, ensuring that our product innovations are data-driven and impactful.

To qualify for the Senior Data Scientist position at FIS Global, you should have a Master's degree in a quantitative field such as Statistics, Data Science, or Computer Science, paired with at least four years of relevant experience. Familiarity with programming languages like Python and SQL, as well as practical knowledge in machine learning techniques, is essential for succeeding in this role.

For the Senior Data Scientist position at FIS Global, you will need to be proficient in programming with Python and SQL. These skills are crucial for developing data pipelines and implementing machine learning models effectively. Knowledge of version control systems, particularly Git, is also essential for collaborating with your team efficiently.

The work environment for a Senior Data Scientist at FIS Global is dynamic and collaborative. You’ll be part of a diverse team that encourages innovation and shares knowledge, thriving in a culture that values contributions from every individual. Flexibility and support for personal and professional growth are also major aspects of the work atmosphere.

At FIS Global, a Senior Data Scientist has ample opportunities for career advancement. With access to cutting-edge technology and the chance to engage with real-world data challenges, you can hone your skills continuously. Additionally, FIS provides resources for personal and professional development, helping you explore various paths within the fintech space.

When discussing your experience with machine learning models, provide specific examples of projects you have worked on, detailing the algorithms you've used and the results achieved. Highlight any challenges faced and how you overcame them, showcasing your problem-solving abilities and technical knowledge.

To ensure data quality, explain your process for data cleaning and validation. Discuss any tools or methodologies you employ to identify and mitigate errors while handling data. Highlight the importance of maintaining data integrity to achieve accurate outcomes from your models.

Mention your expertise in programming languages relevant to the position, such as Python and SQL. Discuss projects where you've utilized these languages and any libraries or frameworks you have experience with, demonstrating your adaptability to different coding environments.

When communicating complex data findings, emphasize your ability to simplify technical concepts for non-technical stakeholders. Use visualizations and clear, straightforward language in your explanations and provide real-world implications for the findings to make them relatable.

Provide a detailed account of a specific instance where you detected an anomaly in your data. Discuss the tools or formulas you used to identify it, the significance of the anomaly, and the steps you took to address or analyze it further, showcasing your analytical skills.

Discuss your dedication to staying informed about industry trends, such as following relevant journals, participating in online forums, attending conferences, and connecting with other professionals. Mention any specific resources you utilize that help you remain up-to-date with advances in data science.

Explain your collaborative approach by discussing the importance of communication, setting clear objectives, and respecting diverse perspectives. Share examples of successful projects where cross-functional collaboration played a key role in achieving the goals.

Discuss your familiarity with cloud platforms like AWS, Azure, or GCP, and how you have utilized these technologies for data storage, processing, and model deployment. Provide any relevant examples of projects that benefited from cloud-based solutions.

Share your background in statistical analysis, including specific techniques you are skilled in, such as regression analysis, hypothesis testing, or time-series analysis. Provide examples of how you've applied these techniques to real-world business problems.

Discuss a particular challenge you’ve encountered in your data science career, such as data scarcity or model bias. Share how you addressed this challenge and the lessons learned, emphasizing your resilience and ability to adapt.