Job Description

Job Description

As part of the Cyber Data Integration Solutions (CDIS) team at Truist, you will work to understand business goals, operations and objectives and use your data science knowledge and experience to identify, explore, and validate new use cases. You will also collaborate with stakeholders to build rapid prototypes and run pilots that test key technical and business assumptions as you develop, implement, and scale up solutions that bring business value and/or reduce risk. You will collaborate with other data scientists as well as other data engineers, software engineers, and UX developers who are part of the CDIS team and who manage the cyber security data warehouse and data fabric (data pipeline design, development, operations, and maintenance), build and update key metrics used for cyber security operations and for executive reporting, and create data visualizations for rapid self-service consumption of key metrics.

In this role you will ultimately be responsible for the following: Perform sophisticated analytics (statistical and predictive analytics, root cause analysis, machine learning modeling, etc.) to provide actionable insights that improve business outcomes and minimize risk and provide consultation to business leaders and other stakeholders on how to leverage analytics insights and build strategies around analytics.

ESSENTIAL DUTIES AND RESPONSIBILITIES
Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time.

1. Independently perform sophisticated data analytics (encompassing data mining, inferential statistical analysis, and predictive analytics, for example) on structured and unstructured data. Identify actionable insights from various (or multiple) sources of data that measurably improve business outcomes or reduce business risk.
2. Produce compelling data visualizations to communicate insights and influence outcomes among a wide array of stakeholders.
3. Work with extract, transform and load (ETL) developers to test and validate data attributes and with other business analysts to ensure data is integrated consistently, regardless of the source system.
4. Analyze information to determine business needs from requests and implement effective technical solutions by providing problem analysis and resolution in a timely manner and explain and interpret complex, difficult, or sensitive information.
5. Analyze available data and find the root cause of a business problem. Develop technical solutions by providing problem analysis and resolution in a timely manner and explain/interpret complex, difficult, or sensitive information. Provide users with progress updates.
6. Drive efforts related to automating repeatable data preparation and integration tasks. Partner with technology teams to understand data capture, testing needs, and to build and test end-to-end solutions.
7. Support new, ongoing, and strategic projects and take accountability and ownership of end-to-end data science solution design, technical delivery, and measurable business outcome.
8. Extract and evaluate information gathered from multiple sources, resolve data conflicts, and customize communication of key data findings, while providing recommendations to diverse audiences.
9. Co-design the optimal solutions to solve the complex business problems with advanced data science capabilities to create business value. Emphasize reusability and scalability in all design, development, deployment, and mentoring activities through implementing consistent coding standards and best practices, focusing on clarity of communication across stakeholder groups and maintaining rigorous documentation.
10. Exercise sound judgment and foster risk management culture throughout design, development, and deployment practices; partner with cross-functional teams to coordinate rules on data usage; data governance and analytics capabilities; help with the overall risk management for the team.
11. Actively research and advocate adoption of emerging methods and technologies in the data science field, with the eye of continually advancing Truist's capabilities.
    
    

QUALIFICATIONS
Required Qualifications:
The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

1. Bachelor's degree in a quantitative field such as Finance, Mathematics, Analytics, Data Science, Computer Science, or Engineering, or equivalent education and related training
2. In-depth knowledge in practices, theories and methodologies associated with the professional discipline such as statistics, programming (such as Python, etc.), machine learning, model development, business information visualization, etc.
3. High level of competency in statistical and analytical principles, tools, and techniques including a broad understanding of classical statistics, probability theory, econometrics, time-series, and primary statistical tests
4. In-depth knowledge of SQL, relational databases, ETL development and concepts, data integration concepts and big data concepts
5. Experience in managing multiple projects with tight deadlines in a collaborative environment.
6. Strong analytical and critical thinking capabilities and ability to multi-task, work independently, and deliver under pressure.

Preferred Qualifications:

1. Master's degree or PhD in a quantitative field such as Finance, Mathematics, Statistics, Econometrics, Analytics, Data Science, Computer Science, or Engineering
2. Four years of relevant work experience if candidate lacks graduate degree
3. Previous experience in the banking or fin-tech industry
4. Experience working with cloud-based architecture (AWS, Snowflake, etc.) and cloud data analytics or computing

If you are interested or have any references please share resume at mukul@brightmindsol.com.

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