Preparing for a Data Analyst job interview requires a strong understanding of data manipulation, statistical analysis, and proficiency in tools like SQL, Excel, and Python. Demonstrating problem-solving skills and the ability to translate complex data into actionable insights is crucial. Emphasizing experience with data visualization and clear communication can set candidates apart from others.
Tell me about yourself.
Focus on highlighting your background in data analysis, including experience with tools like SQL, Python, and Tableau, to demonstrate technical proficiency. Emphasize your familiarity with financial services data and your ability to translate complex datasets into actionable insights, aligning with State Street's emphasis on risk management and investment analytics. Convey your strong problem-solving skills and collaborative approach to support State Street's data-driven decision-making culture.
Do's
- Professional Summary - Provide a concise overview of your education, experience, and key skills relevant to data analysis.
- Quantify Achievements - Mention specific projects or results where you improved data processes or insights with measurable impact.
- Align with State Street Values - Highlight your analytical mindset, attention to detail, and commitment to financial services excellence.
Don'ts
- Personal Details - Avoid sharing unrelated personal information or hobbies that do not support your candidacy.
- Vague Statements - Do not give generic answers without demonstrating concrete skills or experiences.
- Negativity - Refrain from speaking negatively about past employers or job roles during your introduction.
Why do you want to work at State Street?
Emphasize your alignment with State Street's reputation as a global leader in financial services and its commitment to innovation in data analytics. Highlight your enthusiasm for leveraging your analytical skills to contribute to State Street's data-driven decision-making processes and risk management solutions. Showcase your motivation to grow professionally within a collaborative environment focused on cutting-edge technology and financial data insights.
Do's
- Company Research - Demonstrate knowledge of State Street's market position and values.
- Role Alignment - Explain how your skills fit the Data Analyst responsibilities at State Street.
- Career Growth - Highlight your motivation to grow within State Street's data-driven environment.
Don'ts
- Generic Answers - Avoid vague statements that lack specific connection to State Street or the role.
- Salary Focus - Don't mention compensation as the main reason for wanting to work there.
- Negative Remarks - Refrain from criticizing previous employers or appearing overly opportunistic.
Why are you interested in the Data Analyst position?
Emphasize your strong analytical skills and passion for turning complex data into actionable insights that drive business decisions, aligning with State Street's focus on innovative financial services. Highlight your enthusiasm for working with advanced data tools and technologies used by State Street to enhance investment strategies and risk management. Demonstrate your commitment to contributing to a company known for its data-driven culture and global impact in the financial industry.
Do's
- Research State Street - Demonstrate knowledge of State Street's business, culture, and data analytics approach.
- Highlight relevant skills - Emphasize your expertise in data analysis tools, SQL, and statistical methods.
- Align career goals - Connect your career aspirations with the growth opportunities at State Street.
Don'ts
- Be vague - Avoid general statements that show no specific interest in the company or role.
- Focus only on salary - Do not mention compensation as the primary reason for your interest.
- Ignore role requirements - Avoid neglecting the specific responsibilities and expectations of a Data Analyst at State Street.
What experience do you have with data analysis?
Highlight your proficiency in data manipulation tools such as SQL, Python, or R, emphasizing projects where you extracted insights from large datasets to drive business decisions. Mention experience with data visualization software like Tableau or Power BI to communicate complex trends clearly. Showcase specific examples relevant to financial services or investment management, reflecting State Street's industry focus.
Do's
- Highlight Relevant Tools - Mention proficiency with data analysis tools such as SQL, Python, R, Excel, and Tableau.
- Quantify Achievements - Provide specific examples of how your analysis improved business outcomes with measurable results.
- Explain Methodologies - Describe the statistical or analytical methods used to interpret data and derive insights.
Don'ts
- Avoid Vague Responses - Do not give general answers lacking concrete examples or metrics.
- Don't Overlook Soft Skills - Avoid ignoring communication and problem-solving skills related to data interpretation.
- Don't Exaggerate Experience - Avoid overstating your proficiency or project involvement beyond your actual experience.
How do you handle large data sets?
Efficiently handling large data sets requires strong skills in data cleaning, transformation, and analysis using tools like SQL, Python, and Excel. Leveraging State Street's preferred platforms, such as Tableau and Alteryx, enhances data visualization and automation processes, ensuring accuracy and actionable insights. Implementing robust data management techniques and maintaining data integrity supports effective decision-making and aligns with State Street's commitment to data-driven solutions.
Do's
- Data Cleaning - Emphasize methods for identifying and removing inconsistencies, duplicates, and errors in large data sets.
- Efficient Tools - Highlight experience using tools such as SQL, Python, or R to manage and analyze big data efficiently.
- Data Organization - Describe strategies for structuring and indexing data to improve retrieval and processing speed.
Don'ts
- Overgeneralizing - Avoid vague answers that lack specifics on handling complex data challenges.
- Ignoring Performance - Do not overlook the importance of optimizing queries and workflows for large-scale data processing.
- Neglecting Validation - Refrain from underestimating the necessity of verifying data accuracy and integrity.
What statistical methods are you familiar with?
Highlight proficiency in key statistical methods such as regression analysis, hypothesis testing, ANOVA, and time series analysis commonly used in financial data contexts. Emphasize experience with tools like R, Python (pandas, statsmodels), or SAS for implementing these techniques to derive actionable insights. Mention familiarity with descriptive statistics, probability distributions, and data visualization to support data-driven decision-making at State Street.
Do's
- Descriptive Statistics - Explain your experience with measures like mean, median, mode, variance, and standard deviation to summarize datasets effectively.
- Regression Analysis - Highlight your knowledge of linear and logistic regression techniques for predicting outcomes and identifying relationships between variables.
- Hypothesis Testing - Discuss your ability to perform t-tests, chi-square tests, and ANOVA to validate data-driven assumptions and conclusions.
Don'ts
- Overgeneralize Methods - Avoid listing statistical techniques without explaining your practical experience using them in real projects.
- Ignore Business Context - Do not omit how these methods help solve business problems or support decision-making processes at State Street.
- Use Jargon Excessively - Refrain from using complex statistical terms without clarifying their relevance or application in data analysis tasks.
Describe a challenging data analysis project you've worked on.
Highlight a specific data analysis project involving large, complex datasets where you identified key trends or anomalies that impacted business decisions. Emphasize your use of advanced tools such as SQL, Python, or Tableau to clean, analyze, and visualize data efficiently. Showcase your problem-solving skills by explaining how you overcame obstacles like data inconsistency or tight deadlines to deliver actionable insights for stakeholders at State Street.
Do's
- Specific project details - Describe the project context, objectives, and your role clearly and concisely.
- Data tools and techniques - Highlight tools like SQL, Python, R, or Tableau, and analysis methods used to derive insights.
- Problem-solving process - Explain challenges encountered and the systematic approach taken to resolve them effectively.
Don'ts
- Vague descriptions - Avoid general statements without concrete examples or measurable outcomes.
- Technical jargon overload - Refrain from excessive technical terms that may confuse interviewers unfamiliar with specific tools.
- Blaming others - Do not assign fault to team members or external factors instead focus on your contribution and learning.
How do you ensure the accuracy of your results?
To ensure the accuracy of results as a Data Analyst at State Street, consistently apply rigorous data validation techniques such as cross-referencing multiple data sources and conducting thorough data cleaning processes. Leverage advanced Excel functions, SQL queries, and statistical tools like Python or R to detect anomalies and perform error checks. Document every step with clear audit trails and collaborate with team members to review and verify findings before final reporting.
Do's
- Use Data Validation - Apply rigorous data validation methods to verify the accuracy of data inputs and outputs.
- Document Processes - Maintain clear documentation of data analysis workflows for transparency and repeatability.
- Cross-Check Results - Perform cross-checks and peer reviews to confirm the correctness of analytical findings.
Don'ts
- Rely on Assumptions - Avoid making unverified assumptions without supporting data evidence.
- Ignore Anomalies - Do not overlook inconsistencies or outliers in the dataset as they can impact accuracy.
- Skip Testing - Never bypass testing of analytical models or results before delivering them to stakeholders.
What tools and software are you proficient in Excel, SQL, Python, R, Tableau?
Highlight proficiency in Excel functions such as pivot tables, VLOOKUP, and data visualization for efficient data analysis. Emphasize experience writing complex SQL queries for data extraction and database management. Mention practical knowledge of Python libraries like pandas and NumPy, R for statistical analysis, and Tableau for creating interactive dashboards to support decision-making at State Street.
Do's
- Excel - Highlight advanced skills like pivot tables, VLOOKUP, and data visualization to demonstrate proficiency.
- SQL - Emphasize experience with writing complex queries, joins, and data manipulation for efficient database management.
- Python - Mention familiarity with data analysis libraries such as Pandas, NumPy, and visualization tools like Matplotlib or Seaborn.
- R - Show competence in statistical analysis, data modeling, and using RStudio for reproducible research.
- Tableau - Describe ability to create interactive dashboards, perform data storytelling, and derive actionable insights.
Don'ts
- Overgeneralizing skills - Avoid vague terms like "familiar with" without specifying your proficiency or experience level.
- Ignoring job requirements - Do not mention tools irrelevant to the Data Analyst role or State Street company's focus on financial data.
- Exaggerating expertise - Refrain from overstating your abilities which may lead to unrealistic expectations during interviews or on the job.
Explain a time when you identified and solved a data-related problem.
Describe a specific instance where you analyzed a dataset to uncover inconsistencies or errors affecting business insights. Detail the tools and methodologies used, such as SQL for querying databases or Python for data cleaning and visualization, and highlight the steps taken to resolve the issue, ensuring data accuracy and integrity. Emphasize the positive impact on decision-making or process efficiency, demonstrating problem-solving skills relevant to State Street's data-driven environment.
Do's
- Describe the problem clearly - Explain the specific data issue you identified with relevant context and impact on business processes.
- Outline your analytical approach - Detail the methods and tools you used to investigate and understand the problem, such as SQL, Excel, or data visualization software.
- Highlight measurable outcomes - Share quantifiable results or improvements achieved through your solution, demonstrating your contribution to decision-making or efficiency.
Don'ts
- Vague explanations - Avoid general or unclear descriptions of the problem and your role in solving it.
- Ignore teamwork - Don't omit mentioning collaboration with colleagues or stakeholders when relevant to solving the problem.
- Skip follow-up steps - Do not leave out how you monitored or ensured the solution's effectiveness over time.
How do you communicate technical findings to a non-technical audience?
Effectively communicating technical findings to a non-technical audience involves simplifying complex data insights into clear, relatable language without jargon. Use visual aids like charts or dashboards to illustrate key points, ensuring that actionable recommendations align with business objectives. Emphasize the impact of findings on decision-making and operational goals to foster understanding and engagement among stakeholders.
Do's
- Simplify Complex Concepts - Use clear, jargon-free language to make technical findings accessible.
- Use Visuals - Incorporate charts, graphs, and infographics to illustrate key data points effectively.
- Focus on Business Impact - Relate technical insights to business objectives and outcomes relevant to State Street.
Don'ts
- Avoid Technical Jargon - Refrain from using industry-specific terms that may confuse the audience.
- Don't Overwhelm with Data - Present only the most relevant and actionable information to keep the audience engaged.
- Never Assume Prior Knowledge - Ensure explanations are thorough enough for non-technical stakeholders to understand.
Give an example of how you worked as part of a team.
Highlight a specific project where you collaborated with cross-functional teams to analyze large datasets using tools like SQL, Python, or Excel to derive actionable insights. Emphasize your role in communicating findings to stakeholders, ensuring data accuracy and contributing to decision-making processes that improved business outcomes. Demonstrate your ability to work within State Street's data governance and compliance standards while fostering a collaborative environment.
Do's
- Team Collaboration - Describe your role and contributions clearly within the team to demonstrate effective collaboration.
- Problem-Solving - Highlight specific challenges your team faced and how you helped resolve them.
- Data-Driven Impact - Provide examples of how your data analysis supported team decisions or improved project outcomes.
Don'ts
- Overgeneralizing - Avoid vague statements without concrete examples of your teamwork experience.
- Taking Sole Credit - Don't ignore the contributions of other team members or appear to have worked alone.
- Ignoring Role Relevance - Avoid discussing unrelated teamwork experiences that do not showcase your data analyst skills.
Describe your process for cleaning and validating data.
Explain your data cleaning process by detailing steps such as identifying missing or inconsistent data, using tools like Python or Excel for corrections, and applying validation techniques like cross-referencing with original sources or implementing automated checks. Highlight your experience with relevant software such as SQL for querying datasets and data profiling tools to ensure accuracy and completeness. Emphasize your ability to document the cleaning process and communicate findings effectively to maintain data integrity for reliable analytical outcomes at State Street.
Do's
- Data Cleaning - Explain techniques such as handling missing values, removing duplicates, and correcting inconsistencies.
- Data Validation - Describe the use of automated scripts, validation rules, and cross-referencing with trusted sources.
- Tools and Software - Mention familiar tools like SQL, Python (Pandas), or Excel used for efficient data processing and validation.
Don'ts
- Overgeneralizing - Avoid vague statements without specific examples or methodologies.
- Ignoring Data Quality - Do not downplay the importance of accuracy and consistency in data cleaning and validation.
- Technical Jargon Overuse - Avoid excessive technical terms that could confuse non-technical interviewers.
How do you prioritize and manage multiple projects or deadlines?
Effectively prioritize multiple projects by assessing deadlines, business impact, and resource availability, ensuring alignment with State Street's data-driven strategic goals. Utilize project management tools like JIRA or Microsoft Project to track progress and adjust timelines proactively, minimizing risks to key deliverables. Maintain clear communication with stakeholders and leverage data analytics to identify bottlenecks, enabling timely decision-making and efficient workload distribution.
Do's
- Use STAR Method - Structure your answer with Situation, Task, Action, and Result to clearly explain your prioritization process.
- Highlight Time Management - Emphasize your ability to allocate time effectively and meet deadlines for multiple data projects.
- Showcase Tools - Mention project management or analytics tools like Excel, SQL, or JIRA that help you track progress and prioritize tasks.
Don'ts
- Avoid Vague Responses - Do not give generic answers without specific examples related to data analysis or project management.
- Don't Overpromise - Avoid claiming to handle unlimited projects flawlessly; focus on realistic workload management.
- Steer Clear of Negative Comments - Do not blame others or past experiences for missed deadlines or poor prioritization.
What experience do you have in the financial industry?
Highlight your relevant experience analyzing financial data, using tools like SQL, Excel, and Python to extract insights and support decision-making. Emphasize familiarity with financial products, market trends, and regulatory requirements typical of State Street's operations. Showcase any specific projects involving risk assessment, portfolio analysis, or reporting that demonstrate your ability to handle large financial datasets effectively.
Do's
- Relevant Work Experience - Highlight specific roles or projects related to financial data analysis and reporting.
- Technical Skills - Emphasize proficiency in financial modeling, data visualization, SQL, Python, and tools like Excel or Tableau.
- Industry Knowledge - Discuss understanding of financial markets, risk management, or regulatory compliance relevant to State Street.
Don'ts
- Irrelevant Experience - Avoid focusing on unrelated roles outside the financial sector or data analysis scope.
- Vague Answers - Refrain from giving generic statements without concrete examples or metrics to support claims.
- Negative Remarks - Do not criticize past employers or projects; keep the tone positive and professional.
Have you ever worked with confidential or sensitive data?
When answering the question about working with confidential or sensitive data for a Data Analyst role at State Street, highlight your experience handling financial, personal, or proprietary information with strict adherence to data privacy policies and regulatory compliance such as GDPR or HIPAA. Emphasize your use of secure data storage solutions, encryption methods, and access controls to protect sensitive datasets throughout analysis processes. Demonstrate understanding of ethical data management practices and your commitment to maintaining confidentiality while delivering accurate, actionable insights.
Do's
- Confidentiality - Emphasize your commitment to maintaining strict confidentiality when handling sensitive data.
- Data Security Protocols - Describe your experience following industry-standard data security protocols and compliance requirements.
- Ethical Data Handling - Highlight your understanding of ethical considerations and responsible use of confidential information.
Don'ts
- Revealing Specific Data - Avoid sharing exact details or specifics about confidential projects or client information.
- Downplaying Importance - Do not underestimate the significance of protecting sensitive data in your role.
- Overgeneralizing Experience - Avoid vague statements about handling data without concrete examples or processes used.
How do you stay updated with industry trends and best practices?
To stay updated with industry trends and best practices as a Data Analyst at State Street, regularly engage with leading data science and finance publications such as Harvard Business Review, DataCamp, and Bloomberg. Participate in relevant webinars, conferences, and online courses focused on advanced analytics, machine learning, and financial modeling to enhance technical skills. Actively contribute to professional networks like the Data Science Association and State Street's internal knowledge-sharing platforms to exchange insights and stay informed on cutting-edge tools and methodologies.
Do's
- Continuous Learning - Emphasize regular engagement with courses, webinars, and certifications related to data analysis and financial services.
- Industry Research - Highlight subscription to industry journals, blogs, and reports from authoritative sources like Gartner or Forrester.
- Networking - Mention active participation in professional networks, data analytics forums, and State Street community events.
Don'ts
- Relying on Outdated Information - Avoid stating dependence on old textbooks or unverified online content.
- Ignoring Company-Specific Trends - Do not neglect tracking trends specifically relevant to State Street's financial services and analytics needs.
- Overgeneralizing - Steer clear of vague answers that lack specific examples related to data analytics best practices and technological advancements.
Are you familiar with data visualization techniques?
Highlight your experience with key data visualization tools such as Tableau, Power BI, or Excel, emphasizing your ability to transform complex datasets into clear, actionable insights. Describe specific techniques like trend analysis, heat maps, or dashboards, showcasing how you tailor visualizations to support decision-making in financial services. Mention familiarity with State Street's focus on investment management and risk analytics to demonstrate alignment with their data visualization needs.
Do's
- Explain Key Techniques - Describe common data visualization methods like bar charts, line graphs, scatter plots, and heat maps used for analyzing trends and patterns.
- Showcase Tools Expertise - Mention proficiency in tools such as Tableau, Power BI, Excel, or Python libraries like Matplotlib and Seaborn relevant to the Data Analyst role.
- Highlight Business Impact - Emphasize how your visualizations have helped stakeholders make data-driven decisions and improved operational efficiency.
Don'ts
- Avoid Vague Answers - Do not answer without specifics or technical details related to data visualization techniques and tools.
- Do Not Overlook Context - Avoid ignoring the business context or the importance of tailoring visualizations to the audience's needs.
- Steer Clear of Irrelevant Tools - Do not mention visualization tools or techniques unrelated to the Data Analyst position or the company's domain.
Describe a situation where you had to learn a new tool quickly.
Highlight a specific experience where rapid adaptation to new analytics software or data visualization tools, such as SQL or Tableau, was essential to meet project deadlines. Emphasize your approach to self-directed learning through tutorials, online courses, or collaborative support from colleagues, ensuring seamless integration into your workflow. Demonstrate the positive impact of swiftly mastering the tool by explaining improved data insights or reporting accuracy that benefitted the team at State Street.
Do's
- Highlight quick adaptability - Emphasize how you efficiently acquired new skills to meet project deadlines.
- Mention relevant tools - Reference data analysis software like SQL, Python, or Tableau used during the learning process.
- Show problem-solving skills - Describe challenges faced and strategies applied to overcome learning obstacles.
Don'ts
- Avoid vagueness - Do not provide unclear or generic answers without specific examples.
- Don't downplay teamwork - Avoid implying you learned in isolation; mention collaboration if applicable.
- Steer clear of negativity - Do not criticize tools or colleagues during your explanation.
Do you have experience automating data processes?
Highlight specific tools like Python, SQL, or Excel VBA you have used to automate data workflows, emphasizing improvements in efficiency and accuracy. Mention projects where automation reduced manual tasks or accelerated report generation, providing quantifiable outcomes. Connect your skills to State Street's focus on data-driven decision-making and operational excellence in financial services.
Do's
- Highlight specific tools - Mention automation tools like Python, SQL, or Alteryx used in data processing.
- Provide examples - Share concrete examples of projects where data processes were automated successfully.
- Emphasize efficiency gains - Quantify improvements in speed, accuracy, or cost reductions from automation efforts.
Don'ts
- Overgeneralize experience - Avoid vague statements without backing details or specific accomplishments.
- Ignore data quality - Do not overlook how automation maintained or improved data integrity.
- Dismiss collaboration - Refrain from neglecting teamwork involved in implementing automation solutions.
Tell me about a time you received constructive criticism.
Focus on a specific instance where you received constructive criticism related to data analysis, such as feedback on data visualization clarity or coding efficiency. Explain how you objectively assessed the feedback, implemented changes to improve your analytical approach or reporting accuracy, and enhanced overall project outcomes. Highlight the positive impact on team collaboration, data quality, and your professional growth within a financial services context like State Street.
Do's
- Specific Example - Share a clear, relevant instance of receiving constructive criticism related to data analysis or teamwork.
- Positive Attitude - Demonstrate openness to feedback and willingness to improve your skills.
- Actionable Outcome - Explain the steps you took to address the feedback and how it enhanced your performance.
Don'ts
- Vagueness - Avoid generic or unclear responses that lack specific details.
- Defensiveness - Refrain from blaming others or showing resistance to criticism.
- Negative Tone - Do not speak poorly about previous managers or coworkers involved in the feedback process.
How do you handle incomplete or missing data?
When handling incomplete or missing data at State Street, prioritize data imputation techniques such as mean, median, or mode substitution, ensuring the chosen method aligns with the dataset's distribution and business context. Employ data validation tools and audit trails to track data quality and identify missing patterns early in the analysis process. Collaborate with cross-functional teams to source additional data or refine data collection processes, maintaining data integrity and supporting robust analytical outcomes.
Do's
- Data Imputation -Use statistical methods like mean, median, or mode to fill missing values appropriately.
- Data Validation -Verify the accuracy and completeness of data sources before analysis.
- Clear Communication -Explain the impact of missing data on analysis outcomes and decisions transparently.
Don'ts
- Ignoring Missing Data -Avoid proceeding with analysis without addressing or acknowledging gaps in the dataset.
- Assuming Data is Perfect -Never treat incomplete data as reliable without performing necessary checks.
- Overlooking Documentation -Do not fail to document how missing data was handled to maintain reproducibility.
What are your salary expectations?
When responding to the salary expectations question for a Data Analyst position at State Street, research the average industry salary range tailored to your experience level and geographic location using resources like Glassdoor or Payscale. Provide a realistic range based on this data, emphasizing flexibility by stating you are open to discussing compensation in relation to the role's responsibilities and growth opportunities. Highlight your skills and contributions that justify your salary expectations, aligning with State Street's market standards and internal compensation structure.
Do's
- Research Market Rates - Base your salary expectations on current data analyst salaries at State Street and industry standards.
- Express Flexibility - Indicate openness to negotiation while stating a reasonable salary range.
- Highlight Value - Connect your skills and experience to justify your salary expectations effectively.
Don'ts
- Avoid Giving a Specific Number First - Do not state a fixed figure before understanding the full job responsibilities and benefits.
- Don't Undervalue Yourself - Avoid quoting a salary that does not reflect your expertise and market value.
- Don't Focus Solely on Salary - Avoid making it seem like compensation is your only priority; consider overall job fit and growth opportunities.
Why should we hire you for this position?
Highlight your proficiency in data analysis tools such as SQL, Python, and Tableau, emphasizing your ability to transform complex datasets into actionable insights driving business decisions. Showcase your experience with financial data trends and risk assessment, aligning with State Street's focus on asset management and financial services. Demonstrate strong problem-solving skills, attention to detail, and commitment to improving data accuracy and reporting efficiency, which are critical for supporting State Street's client-driven strategies.
Do's
- Highlight relevant skills - Emphasize your proficiency in data analysis tools like SQL, Python, and Excel.
- Showcase problem-solving abilities - Provide examples of how you have used data to drive business decisions or improve processes.
- Align with company values - Mention your understanding of State Street's mission and how you can contribute to its goals.
Don'ts
- Be vague - Avoid generic answers that don't specify your unique qualifications or experience.
- Overstate experience - Do not exaggerate your skills or projects, as honesty is crucial.
- Focus solely on personal gain - Don't emphasize benefits to yourself without linking them to how you will add value to State Street.
Do you have any questions for us?
When asked "Do you have any questions for us?" in a Data Analyst interview at State Street, focus on inquiring about the company's data analytics tools and technologies, team collaboration practices, and opportunities for professional development. Questions like "Can you describe the data infrastructure and analytics platforms used at State Street?" or "How does the team ensure data quality and accuracy in reporting?" demonstrate your interest and insight into the role. This approach shows you are proactive, technically engaged, and eager to contribute effectively to State Street's data-driven decision-making.
Do's
- Ask about team structure - Inquire how the Data Analytics team is organized and collaborates within State Street.
- Discuss data tools - Question which data management and analysis tools are most commonly used for projects.
- Understand company goals - Seek details on State Street's strategic goals related to data and analytics innovation.
Don'ts
- Avoid salary questions - Do not bring up compensation in the initial interview stage unless prompted by the interviewer.
- Skip vague queries - Refrain from asking unclear or generic questions that do not reflect research about the company.
- Ignore cultural fit - Avoid neglecting questions about company culture and values which are important for alignment.