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Senior Analyst - Data Science - job 3 of 4

You Lead the Way. We’ve Got Your Back.

With the right backing, people and businesses have the power to progress in incredible ways. When you join Team Amex, you become part of a global and diverse community of colleagues with an unwavering commitment to back our customers, communities and each other. Here, you’ll learn and grow as we help you create a career journey that’s unique and meaningful to you with benefits, programs, and flexibility that support you personally and professionally.

At American Express, you’ll be recognized for your contributions, leadership, and impact—every colleague has the opportunity to share in the company’s success. Together, we’ll win as a team, striving to uphold our company values and powerful backing promise to provide the world’s best customer experience every day. And we’ll do it with the utmost integrity, and in an environment where everyone is seen, heard and feels like they belong.

Join Team Amex and let's lead the way together.

Our industry is rapidly evolving, and we need courageous, quick thinkers who can shape the strategic decisions that lead our business forward. Whether it’s negotiating with some of our largest global partners or creating next year’s financial plan, you can influence both our day-to-day P&L and the future direction of the company. As part of the team, you can have the opportunity to learn and use the latest data tools and technologies and explore a range of roles to grow your career. Find your place in finance on #TeamAmex. 

Finance Data Science & Analytics team’s focus is to build predictive models grounded in data and leverage Machine Learning techniques to add stability and predictability to the AXP P&L. FDS team’s goal is to help the organization make fact-based and scientifically developed decisions around risk and opportunities.

Responsibilities:

  • Lead the design, planning, and execution of machine learning projects, ensuring alignment with the company’s strategic goals and objectives.
  • Innovate with a focus on developing newer and better revenue models using big data and machine learning solutions.
  • Design, develop, and deploy machine learning models to predict spending and lending patterns, delivering value to finance colleagues and business partners.
  • Leverage causal inference framework to determine drivers and their contribution in revenue growth
  • Utilize advanced data analysis and visualization techniques to explain model outputs and extract insights that support decision-making processes.
  • Collaborate with key stakeholders, including senior management, partners, and the model validation group, to drive the success of machine learning projects.
  • Lead model documentation efforts and partner with the model validation team to ensure compliance with regulatory guidelines.

Minimum Qualifications:

  • Minimum of 2+ years of experience in data science or machine learning, with a focus on developing and deploying machine learning models.
  • BS/MS or PhD in a quantitative field (Computer Science, Statistics, Mathematics, Physics, Chemistry, Operations Research, etc.) with hands-on experience leveraging sophisticated analytical and machine learning techniques.
  • Expertise in an analytical language (Python, R, or the equivalent), and experience with databases (Hive, SQL, or the equivalent) is required.
  • Demonstrated ability to frame business problems into mathematical programming problems, leverage external thinking and tools (from academia and/or other industries) to engineer a solution and deliver business insights.
  • Ability to work effectively in a team environment.
  • Independent thinker who is organized, has great attention to detail, and can multi-task.
  •  Strong communication skills, Ability to integrate with cross-functional business partners worldwide.
  •  Ability to learn quickly and work independently with sophisticated, unstructured initiatives. 

Preferred Qualifications:

  • Experience with data visualization is a plus.
  • Machine Learning Knowledge: Deep understanding of machine learning/statistical algorithms such as deep learning, boosting, and causal inference. 

Considerations for sponsorship:

Depending on factors such as business unit requirements, the nature of the position, cost and applicable laws,

American Express may provide visa sponsorship for certain positions.

Salary Range: $55,000.00 to $105,000.00 annually + bonus + benefits

The above represents the expected salary range for this job requisition. Ultimately, in determining your pay, we’ll consider your location, experience, and other job-related factors.

We back our colleagues and their loved ones with benefits and programs that support their holistic well-being. That means we prioritize their physical, financial, and mental health through each stage of life. Benefits include:

  • Competitive base salaries 
  • Bonus incentives 
  • 6% Company Match on retirement savings plan 
  • Free financial coaching and financial well-being support 
  • Comprehensive medical, dental, vision, life insurance, and disability benefits 
  • Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need 
  • 20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy 
  • Free access to global on-site wellness centers staffed with nurses and doctors (depending on location) 
  • Free and confidential counseling support through our Healthy Minds program 
  • Career development and training opportunities

For a full list of Team Amex benefits, visit our Colleague Benefits Site.

American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law. American Express will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable state and local laws, including, but not limited to, the California Fair Chance Act, the Los Angeles County Fair Chance Ordinance for Employers, and the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance. For positions covered by federal and/or state banking regulations, American Express will comply with such regulations as it relates to the consideration of applicants with criminal convictions.

We back our colleagues with the support they need to thrive, professionally and personally. That's why we have Amex Flex, our enterprise working model that provides greater flexibility to colleagues while ensuring we preserve the important aspects of our unique in-person culture. Depending on role and business needs, colleagues will either work onsite, in a hybrid model (combination of in-office and virtual days) or fully virtually.

US Job Seekers/Employees - Click here to view the “Know Your Rights” poster and the Pay Transparency Policy Statement.

If the links do not work, please copy and paste the following URLs in a new browser window: https://www.dol.gov/agencies/ofccp/posters to access the three posters.

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Average salary estimate

$80000 / YEARLY (est.)
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$55000K
$105000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Senior Analyst - Data Science, American Express

Join the dynamic team at American Express as a Senior Analyst - Data Science in the vibrant city of New York! Here, you'll lead the way in shaping strategic decisions through innovative data-driven insights. At American Express, we believe in backing our colleagues in their unique journeys with exceptional benefits and a commitment to personal and professional growth. As a Senior Analyst, you'll spearhead machine learning projects, collaborated with various stakeholders, and develop predictive models capable of influencing our financial goals. You'll get to utilize cutting-edge data tools while designing and deploying models that predict spending patterns, helping drive success across the company. Your expertise in data analysis will be pivotal in providing clarity and strategic direction to our teams. Moreover, this is not just another job; join a supportive culture that values integrity and fosters a sense of belonging. In the ever-evolving finance landscape, your role will be critical in ensuring our future is stable and prosperous. Embrace a flexible working environment where creativity thrives and innovation is the norm. Dive into a career where your contributions will be recognized and rewarded, and where growth opportunities are plentiful. Let’s lead the way together at American Express!

Frequently Asked Questions (FAQs) for Senior Analyst - Data Science Role at American Express
What are the responsibilities of a Senior Analyst - Data Science at American Express?

As a Senior Analyst - Data Science at American Express, you will lead the design, planning, and execution of machine learning projects that support the company's strategic objectives. Your main responsibilities will include innovating revenue models using big data, predicting spending patterns, leveraging causal inference frameworks, collaborating with key stakeholders, and ensuring regulatory compliance in model documentation. You'll be instrumental in extracting insights that drive decision-making processes.

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What qualifications are needed to become a Senior Analyst - Data Science at American Express?

To qualify for the Senior Analyst - Data Science position at American Express, applicants must possess a minimum of 2 years of relevant experience in data science or machine learning. A BS, MS, or PhD in a quantitative field such as Computer Science or Statistics is required, along with proficiency in analytical languages like Python or R and database experience. Strong analytical skills, the ability to work independently and collaboratively, and excellent communication skills are essential.

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How does American Express support the career growth of Senior Analysts - Data Science?

American Express is deeply committed to the career development of its Senior Analysts - Data Science. The company offers a range of training opportunities, access to cutting-edge data tools, and the chance to work in a flexible environment that promotes innovation. Your contributions will be recognized and rewarded, and you’ll have the opportunity to engage in various roles that can further expand your career trajectory within the organization.

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What types of projects might a Senior Analyst - Data Science work on at American Express?

Senior Analysts - Data Science at American Express might work on a variety of projects aimed at enhancing financial performance and customer experience. This could include developing predictive models to forecast spending, leveraging machine learning to analyze customer behavior, and collaborating on cross-functional projects with diverse teams to innovate and refine finance strategies using advanced data analysis and statistical techniques.

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What is the salary range for a Senior Analyst - Data Science at American Express?

The salary range for a Senior Analyst - Data Science at American Express varies based on experience and location but generally falls between $55,000 and $105,000 annually. In addition to the base salary, you can expect potential bonuses and a comprehensive benefits package that includes medical, dental, retirement savings, and career development opportunities.

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Common Interview Questions for Senior Analyst - Data Science
Can you describe a machine learning project you have led?

When addressing this question, focus on detailing your role, the project's objectives, the methodologies you used, and the outcomes. Highlight key challenges you faced and how you overcame them, showcasing your leadership and technical skills.

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How do you approach problem framing in data science projects?

In your response, explain the importance of understanding business objectives first. Describe how you translate key business questions into data-driven hypotheses, and provide an example of a time you successfully framed a problem that led to meaningful insights.

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What machine learning models are you most familiar with?

Be ready to discuss various machine learning models such as regression, decision trees, and neural networks. Explain your rationale for selecting certain models for specific projects, including performance metrics you consider when evaluating model success.

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How do you ensure compliance with regulatory guidelines in your models?

When answering this question, discuss your experience establishing model documentation processes and collaborating with validation teams. Talk about the specific regulatory guidelines you are familiar with and how you incorporate them into your project workflows.

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Describe a time you used data visualization to tell a story.

Use this opportunity to provide a detailed example illustrating how you utilized data visualization tools to communicate complex data insights effectively. Explain your thought process, what tools you used, and how the audience reacted to your presentation.

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How do you prioritize multiple projects with tight deadlines?

Share your strategies for time management and prioritization skills, such as using project management tools or methodologies. Illustrate with a specific instance where you successfully managed competing deadlines while maintaining the quality of your work.

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What steps do you take to validate your machine learning models?

Discuss your approach to model validation, including techniques such as cross-validation, holdout datasets, and performance metrics. Provide an example of how validation helped improve a model's performance in your previous projects.

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How do you handle feedback on your data analysis?

Emphasize your openness to constructive feedback. Discuss how you have previously received feedback on your work and how you used that information to improve your analysis, focusing on specific instances that illustrated growth.

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What influence do you think data analytics has on financial strategy?

Discuss the pivotal role of data analytics in informing financial strategies. Provide examples of how data-driven insights can lead to optimizing budgeting, forecasting, risk assessment, and strategic decision-making in finances, tying it back to impactful outcomes.

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What tools and technologies are you proficient in for data analysis?

Detail your experience with various data analysis tools and technologies, including programming languages like Python and R, database knowledge with SQL, and any data visualization platforms you’ve used. Explain how these tools have facilitated your projects.

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American Express is a multinational financial services corporation and global leader in providing personal, small business, and corporate credit cards.

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Badge Family FriendlyBadge Office VibesBadge Work&Life BalanceBadge Rapid Growth
CULTURE VALUES
Inclusive & Diverse
Empathetic
Collaboration over Competition
Growth & Learning
Transparent & Candid
BENEFITS & PERKS
Medical Insurance
Dental Insurance
Mental Health Resources
Life insurance
Disability Insurance
Child Care stipend
Employee Resource Groups
Learning & Development
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Full-time, hybrid
DATE POSTED
April 3, 2025

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