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Senior Data Scientist - 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.

As part of our diverse tech team, you can architect, code and ship software that makes us an essential part of our customers’ digital lives.  Here, you can work alongside talented engineers in an open, supportive, inclusive environment where your voice is valued, and you make your own decisions on what tech to use to solve challenging problems.  Amex offers a range of opportunities to work with the latest technologies and encourages you to back the broader engineering community through open source.  And because we understand the importance of keeping your skill fresh and relevant, we give you dedicated time to invest in your professional development. Find your place in technology on #TeamAmex.  

Let’s share success.

Infrastructure Data & Analytics aims to establish and reinforce a culture of effective metrics, data-driven business processes, architecture simplification, and cost transparency. 

As the Data Science team in Global Infrastructure Product Services embarks on building and deploying advanced data science and machine learning (ML) models, we are looking for a senior data scientist to build, deploy and monitor models in production.  This is an individual contributor role with responsibilities ranging from hand-on coding, debugging, documentation to stakeholder management and presentations.

A successful data scientist knows that delivering on that promise takes foresight, planning and agility.  We are seeking data scientists who are not only technically adept, but also understand the importance of harnessing the power of data to streamline financial operations, enhance decision-making and improve business outcomes. 

This team is focused on developing and maintaining dynamic technology infrastructure cost allocation and production models. The financial forecasting models will help ensure that our technology spending is transparent, efficient, and properly aligned with our strategic objectives. The role requires a blend of technical prowess in data engineering, data science, MLOps, and an understanding of enterprise infrastructure components & their economics.

Let’s build on what you know.

If you are a pioneer in developing and continuously evolving data science models in production that drive and monitor infrastructure and cost consumption analysis, you'll find a fit in our Data Science team in Global Infrastructure Product Services. To succeed in this newly forming team, you’ll need to be comfortable navigating ambiguity to stand up solutions 0à1 while discovering and leveraging enterprise platforms and best practices.

Here’s just some of what you’ll do:

  • Design and build scalable solutions using AI and Machine Learning (Generative AI, Natural Language Processing, Reinforcement Learning, and Time Series predictions) in document understanding, financial forecasting, and optimizing infrastructure resource utilization.
  • Own the model building and refining from identifying business problems, hands-on coding to curate training dataset, analyze data & validate models to delivering production grade solutions at scale.
  • Drive high-level and detailed technical design conversations.
  • Own and deliver documentation and methods required for model review with the model governance teams.
  • Be responsible for health and quality of the code across the portfolio, including leadership over innovation, functional testing, and CI/CD tool integration
  • Work closely with stakeholders across Technology, Finance and business unit portfolio leaders to define scope, requirements and incorporate cost drivers, allocation methods, and infrastructure nuances
  • Ensure best practices are aligned with Enterprise Architecture and Decision Science COEs
  • Develop SQL queries, scripts and routines to automate data processing and enhance the model’s accuracy and efficiency
  • Generate insightful data visualization and reports to aid in decision-making
  • Lead training sessions and create comprehensive documentation to empower end users to leverage the cost model effectively
  • Provide technical mentorship to team members at junior levels
  • Function as an active member of an agile team

Are you up for the challenge?  Here’s what you should have:

  • Master’s degree in Data Science, Mathematics, Computer Science, Engineering, Information Systems, or related STEM fields
  • 3+ years full lifecycle product development and engineering leadership in highly available, highly scalable, and high throughput systems
  • Experience in deploying out-of-the box LLMs and Generative AI solutions including setting up Vector DBs, Graph-RAG and extensive testing of LLMs. Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies. Must demonstrate deep knowledge of testing frameworks for Generative AI
  • Demonstrated experience with knowledge graphs
  • Experience in building and owning data science solutions and pipelines within the MLOps ecosystem in Vertex AI
  • Hands-on expertise with handling distributed (multi-tiered) systems and automated testing – unit and performance testing
  • Demonstrated experience in building and deploying a diverse set of Machine Learning (GLM, GBM, Neural Networks), Time series forecasting models and NLP solutions at scale
  • Proficiency and Hands-on Experience with Git, CI/CD tools, Elasticsearch, and a solution in each category: Orchestration, Container, Model Serving, Observability, and Feature Store
  • Strong proficiency in programming with Python, Machine Learning libraries and APIs (TensorFlow, Keras, PyTorch, ScikitLearn, H20.ai, XGBoost)
  • Experience in data visualization and observability with a focus on real time serving and monitoring of time series data with alerts
  • Strong project management skills and effective stakeholder management skills, coupled with a continuous improvement mindset
  • Excellent presentation and communication skills, capable of explaining complex technical choices in simple terms to a diverse audience
  • Experience in Financial Services industry is preferred
  • Thorough understanding of enterprise infrastructure technologies (Compute, Storage, Network, Mainframe) to inform model development is preferred.

Salary Range: $90,000.00 to $165,000.00 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.

Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.

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$90000K
$165000K

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What You Should Know About Senior Data Scientist, American Express

Join American Express as a Senior Data Scientist in New York, New York, where you will be a key player in leading data-driven solutions that enhance decision-making for our financial operations. In this pivotal role, you’ll dive deep into the world of advanced data science and machine learning, developing and deploying cutting-edge models that influence significant business outcomes. You'll have the opportunity to design scalable solutions utilizing AI and machine learning technologies to solve real business challenges such as financial forecasting and infrastructure optimization. Collaborating with a diverse team of engineers, you'll engage in hands-on coding, model building, and stakeholder management, ensuring that our technology infrastructure remains transparent and efficient. With your expertise in MLOps, programming with Python, and understanding enterprise components, you'll own the complete lifecycle of data science solutions, from identifying the right problems to delivering production-ready models. The culture at American Express emphasizes inclusivity and innovation—here, your contributions will be recognized, and you will have access to resources for personal and professional growth. If you're passionate about leveraging data and making an impact, come share your expertise as part of a team that values continuous improvement and collaborative success. Together, let’s lead the way in transforming the financial services landscape.

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

As a Senior Data Scientist at American Express, you'll design and develop scalable machine learning models, handle coding and debugging, and manage stakeholder communications. Your role involves building and deploying AI-driven solutions, particularly focusing on financial forecasting and resource optimization, all while overseeing the health and quality of the deployed models.

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What qualifications are required for the Senior Data Scientist position at American Express?

To qualify for the Senior Data Scientist role at American Express, candidates should possess a Master's degree in Data Science, Mathematics, Computer Science, or related fields, along with a minimum of three years of experience in full lifecycle product development. Familiarity with MLOps, AI model deployment, and programming in Python is also crucial.

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How does American Express support the professional development of its Senior Data Scientists?

At American Express, professional development for Senior Data Scientists is a priority. The company offers dedicated time for skill upgrading, access to cutting-edge technologies, and training sessions that empower team members to continuously enhance their expertise and contribute to impactful projects.

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What technologies will I work with as a Senior Data Scientist at American Express?

In the Senior Data Scientist role at American Express, you'll work with advanced technologies like Generative AI, Natural Language Processing (NLP), and various machine learning frameworks such as TensorFlow and PyTorch. You'll also be involved in using tools for data processing, automation, and model governance, all while collaborating with a tech-savvy team.

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What is the work culture like for Senior Data Scientists at American Express?

The work culture at American Express for Senior Data Scientists is innovative and inclusive. You'll find a collaborative environment that encourages open communication, values diverse perspectives, and prioritizes both individual contributions and team success, all while upholding the company's core values.

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Common Interview Questions for Senior Data Scientist
Can you explain a machine learning project you have worked on?

When answering this question, focus on the specific problem your project aimed to solve, detailing the data you used, the methodologies you applied, and the outcomes. Highlight any challenges faced and how your solutions contributed to the project's success.

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How do you validate the performance of your machine learning models?

Discuss the metrics you prefer to use for validation, such as accuracy, precision, and recall. Provide examples of different validation techniques like cross-validation, train-test splits, and how you handle imbalanced datasets, ensuring that your audience understands the importance of model evaluation.

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What experience do you have with deploying machine learning models into production?

Describe your experience with MLOps and the tools you’ve utilized for deployment. Highlight how you have managed scaling, maintenance, and monitoring of deployed models, as well as any deployment pitfalls you successfully navigated, emphasizing best practices.

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How do you handle missing data in your datasets?

Outline your approach to missing data, including techniques such as imputation, data augmentation, and when to drop missing values. Consider including real-world examples to illustrate how your methods have led to improved model performance.

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Explain your experience with SQL and data manipulation?

Discuss your proficiency in SQL and how you've used it for data extraction and manipulation. Share specific scenarios where your SQL skills played a crucial role in your analysis and model-building processes.

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What are some challenges you've faced in data science projects?

Be ready to discuss specific challenges you've encountered, such as data quality issues, model overfitting, or stakeholder miscommunication. Emphasize the solutions you implemented and what you learned from the experience.

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How do you stay updated with the latest developments in data science?

Explain your strategies for continuous learning—whether it's attending workshops, following relevant publications and blogs, or participating in online communities. Highlighting your commitment to staying current reflects positively on your motivation and adaptability.

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Can you give an example of how you've used data visualization to communicate insights?

Discuss a specific instance where you utilized data visualization tools to present findings to stakeholders. Detail the impact of your visualizations on decision-making processes, emphasizing clarity and storytelling in your approach.

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What role does cross-functional collaboration play in your work?

Highlight the importance of working with various teams such as engineering, finance, and business stakeholders. Share examples of successful collaborations and how they’ve led to improved project outcomes, demonstrating your ability to bridge different perspectives.

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Describe a time when you had to persuade stakeholders to adopt your recommendations.

Provide a detailed account of how you identified key stakeholder concerns and tailored your communication to address them. Open and persuasive communication, backed by data, is critical in influencing decisions effectively, so ensure your example reflects this.

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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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