WHAT IS A DATA SCIENTIST?
Updated: Jun 11, 2025 - The Data Scientist will operate within the Data Analytics team, leveraging diverse data sources and analytical methods to develop solutions aimed at understanding, predicting, and influencing the behavior of both current and potential customers. This role is supported by the Data Engineering team, which provides the necessary analytical and data warehouse infrastructure.
Need-to-Know Overview of a Data Scientist
1. Data Scientist Roles and Responsibilities
- Partner with business to identify opportunities and develop data-driven solutions to improve business outcomes.
- Partner with an internal data services team to ensure proper data is captured in analytical environments, establish data pipelines, implement models, and monitor outcomes.
- Partner with Innovation and Digital transformation team to build new digital products and analytic models for assessing the effectiveness of digital solutions to improve user experience.
- Work in Microsoft Azure and SQL Server environments to develop models.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop and experiment with new algorithms and models to maximize long-term member value.
- Leverage data and insights to inform decision making on some of the most critical topics around acquisition, retention, personalization, and product development.
- Creatively synthesize internal data (bank account information, demographic information) with outside data sources to develop meaningful insights.
- Present findings and solutions to various levels of management.
- Stay updated with industry and data science technology trends.
2. Data Scientist Skills, Abilities and Experience
- Undergraduate degree in Mathematics, Statistics, Computer Science, or a related field
- Master’s degree in Mathematics, Statistics, Computer Science, or a related field
- Data science experience in the banking sector
- Knowledge and experience in applying statistical and machine learning methods including regression, classification, clustering, decision trees, and neural networks
- Experience in developing, deploying, and managing analytical and machine learning models in a production environment
- Ability to design, deploy, monitor, and optimize algorithms and models
- Proven experience in designing and developing machine learning and AI models
- Proficiency in a statistical programming language such as Python or R
- Familiarity and experience working with relational databases and SQL
- Natural sense of curiosity; being able to self-motivate and proactively find unique or actionable insights from data
- Ability to understand business needs and become a leader in enabling data driven business decisions while also having the ability to work successfully in a team environment
- Comfortable working in a dynamic environment with multiple concurrent projects
- Strong communication skills
- Familiarity with unstructured and non-relational data stores
- Familiarity with Azure environment (DevOps, ADLS, Data Bricks)
Career Overview FAQs
What does a career overview explain?
A career overview explains what a profession involves, including the purpose of the role, the type of work professionals typically perform, and how the position contributes to organizations or industries. It helps readers understand the field before exploring more detailed resources such as job responsibilities, required skills, and career requirements.
What do professionals in this career typically do?
Professionals in this field usually manage projects or tasks, collaborate with teams, analyze information, and solve problems related to business or organizational objectives. Daily responsibilities may vary depending on experience level, specialization, and the organization where the role is performed.
What skills are commonly required for this career?
Most careers require a combination of technical skills related to the profession and professional skills such as communication, teamwork, problem solving, and analytical thinking. These abilities help professionals perform effectively in different work environments and across various organizational roles.
What is the typical career path in this field?
Many professionals begin in entry-level roles where they gain foundational knowledge and practical experience. Over time, they may progress to mid-level or senior positions that involve greater responsibility, specialization, or leadership within their organization or industry.
How can someone start a career in this field?
Starting a career in this field typically involves building relevant knowledge, gaining practical experience, and developing the key skills associated with the profession. Preparing strong application materials, such as a targeted resume and cover letter, can also help candidates pursue entry-level opportunities successfully.
Editorial Process
Lamwork content is developed through structured review of publicly available job postings and documented hiring trends.
Editorial operations are managed by Thanh Huyen, Managing Editor, with research direction and final oversight by Lam Nguyen, Founder & Editorial Lead. Content is periodically reviewed to reflect observable labor market changes.