Sr. Data Analyst (Risk/Fraud)

In the role of Senior Data Analyst (Risk/Fraud) at Dave, your primary responsibilities will include conducting in-depth analyses of large datasets to identify fraud patterns and trends, collaborating with cross-functional teams to uncover new business opportunities, and developing data-driven strategies to mitigate risks. You will also be expected to communicate complex findings and recommendations clearly to various business units, ensuring all stakeholders understand the insights derived from the data.

To qualify for the Senior Data Analyst (Risk/Fraud) position at Dave, candidates should possess over 4 years of quantitative analysis experience, ideally in a risk or fraud context. Familiarity with product analytics, proficiency in SQL, and experience with business intelligence tools are essential. A Bachelor’s degree in a STEM field is preferred, along with the ability to work collaboratively across teams and a strong analytical mindset.

Essential skills for the Senior Data Analyst (Risk/Fraud) role at Dave include a strong analytical and problem-solving ability, proficiency in SQL and various data-query languages, and familiarity with tools like Tableau or Looker. Experience in risk/fraud analysis and knowledge of fraud types, such as card transactions or identity theft fraud, will also be critical to success in this position.

The Senior Data Analyst (Risk/Fraud) role at Dave is vital for supporting business decisions as it involves analyzing complex datasets to provide actionable insights regarding fraud patterns and risks. Your analysis will drive the development of strategic initiatives, product launches, and decisions around A/B testing, which directly impact how Dave approaches fraud prevention and enhances user experience.

Dave offers a virtual-first work culture that promotes flexibility and transparency for Senior Data Analysts (Risk/Fraud). Team members can work from anywhere in the U.S. while enjoying a collaborative environment that values diverse perspectives and fosters professional growth. With benefits aimed at supporting your wellbeing and work-life balance, Dave strives to create an inclusive space where everyone feels empowered to contribute.

When answering this question, focus on specific projects where you successfully analyzed large datasets to uncover fraud patterns. Discuss tools you used, like SQL or Python, and the methodologies you applied to ensure accuracy. Highlight any measurable outcomes, such as reduced fraud incidents or improved risk assessment processes.

It's essential to convey your time management skills here. Discuss how you assess urgency, stakeholder needs, and overall project impact when prioritizing projects. Mention any tools or methodologies you use, such as Agile or Kanban, to keep track of progress and deadlines.

Demonstrate your communication skills by providing examples of how you translate technical data into actionable insights. Discuss using visual aids like dashboards, graphs, and presentations to simplify complex information, ensuring non-technical stakeholders can understand and engage with your findings.

Share a specific example where you faced significant hurdles, such as incomplete data sets or complex variables. Describe the analytical strategies (like data cleaning or alternative methodologies) you employed to tackle these challenges, ultimately leading to successful insights that added value.

Your answer should reflect a thorough understanding of A/B testing methodology. Provide examples of A/B tests you've conducted, what hypotheses you were testing, and the impact those results had on product decisions or fraud mitigation strategies. Emphasize the importance of statistical significance in your findings.

When discussing your tools, mention specific software like SQL for data querying, Tableau for visualization, and any programming languages like Python or R that you are accustomed to using. Share examples of how utilizing these tools has enhanced your analysis and provided actionable insights.

Explain your approach to investigating discrepancies, emphasizing a systematic method. This could include verifying data sources, revisiting analytical methods, and cross-referencing with other datasets. Showcase how you maintain accuracy and integrity in your analysis.

Here, reflect on your industry knowledge and continue by discussing emerging trends such as machine learning for predictive analysis in fraud detection. Mention any recent developments you follow, like the integration of AI to enhance risk assessments, underscoring the importance of staying current in the evolving landscape.

To effectively answer this question, share your admiration for Dave's mission and values, particularly how they align with your professional goals. Emphasize your interest in leveraging data analytics to drive meaningful change in the financial sector and your excitement about the potential to contribute to a company that cares about people.

Discuss your approach to continuously learning and adapting your analyses based on industry trends and business needs. Highlight the importance of stakeholder feedback and collaboration in maintaining relevance in your work, ensuring that your insights are aligned with the company's overarching goals and objectives.