Data Scientist

hace 3 semanas

Ciudad de México Amex A tiempo completo

**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 to grow your career.

**How will you make an impact in this role?**

International Consumer Services (ICS) - Strategic Planning - Business Intelligence (BI) & Data Governance:
As Data Scientist in Amex you will work on cutting edge science and technology around Deep Learning, Data Science, NLP, and Cloud technologies. The team works on challenging research problems in domains such as acquisition and marketing services, field force performance, customer performance, spend analysis, and many more. The key goal is to progress the state of the art in science and develop products to solve business problems.

**Responsibilities**:

- Ideate and contribute to innovations to help solve business problems
- Read, understand papers in AI and ML and use the concepts to come up with novel ideas
- Perform POC on Ideas and concepts to test feasibility
- Contribute to product strategy and roadmap; recommend the nature and scope of present and future state of the AI Platforms
- Innovate with a focus on developing newer and better approaches using big data & machine learning solution
- Analyze large amounts of data to derive business insights and create innovative solutions
- Drive billing, revenue growth and profitability through advanced analytical techniques
- Maximize business returns by institutionalizing efficient and accurate models
- Help in the creation of the omnichannel platform to share the necessary tools to effectively communicate insights to stakeholders
- Collecting and analyzing data form various sources to prove insights on business performance, trends and opportunities
- Ensuring data quality, accuracy, and completeness

**Business Outcomes**:
1. Data Source Identification: Research and identify relevant data sources both within the organization (e.g., databases, data warehouses, APIs) and external sources (e.g., market data providers, industry reports) to collect the required data. Evaluate the suitability and reliability of each data source

3. Data Integration: Design and implement data integration processes and workflows to consolidate data from multiple sources into a centralized system or data warehouse. Employ appropriate tools and technologies for data integration, such as ETL (Extract, Transform, Load) processes, data pipelines, or data integration platforms.

4. Data Governance: Develop and implement data governance practices to ensure data quality, integrity, and security throughout the data collection and integration processes. Define data standards, naming conventions, and data access controls to maintain data consistency and protect sensitive information.

5. Data Validation and Quality Assurance: Perform data validation and quality checks to ensure the accuracy and reliability of the integrated data. Identify and resolve data anomalies, inconsistencies, or missing values. Collaborate with relevant stakeholders to address data quality issues.

6. Data Documentation: Maintain comprehensive documentation of the data collection and integration processes, including data source documentation, data flow diagrams, and data mapping. Document data transformations, business rules, and any assumptions made during the integration process.

7. And more

**Qualifications**

Proven analytical skills, including ability to consolidate and transform large amounts of data from different sources into insights and executive BI Solutions and reports:

- High level of integrity when handing confidential information
- Demonstrated ability to learn quickly with a willingness to continuously im

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