Machine learning and GEN AI Data Scientist

About CITI

Citi's mission is to serve as a trusted partner to our clients by responsibly providing financial services that enable growth and economic progress. We have 200 years of experience helping our clients meet the world's toughest challenges and embrace its greatest opportunities.

Analytics and Information Management (AIM)

Citi AIM was established in 2003, and is located across multiple cities in India - Bengaluru, Chennai, Pune and Mumbai. It is a global community that objectively connects and analyzes information, to create actionable intelligence for our business leaders. It identifies fact-based opportunities for revenue growth in partnership with the businesses. The function balances customer needs, business strategy, and profit objectives using best in class and relevant analytic methodologies.

What do we do?

The North America Consumer Bank - Data Science and Modeling team analyzes millions of prospects and billions of customer level transactions using big data tools and machine learning, AI techniques to unlock opportunities for our clients in meeting their financial needs and create economic value for the bank.

The team extracts relevant insights, identifies business opportunities, converts business problems into modeling framework, uses big data tools, latest deep learning and machine learning algorithms to build predictive models, implements solutions and designs go-to-market strategies for a huge variety of business problems.

Role Description

• The role will be in the Data Science and Modeling of North America Consumer Bank team
• The role will report to the AVP / VP leading the team

What do we offer: The Next Gen Analytics (NGA) team is a part of the Analytics & Information Management (AIM) unit. The NGA modeling team will focus on the following areas of work:

Role Expectations:

• Client Obsession - Create client centric analytic solution to business problems. Individual should be able to have a holistic view of multiple businesses and develop analytic solutions accordingly.
• Analytic Project Execution - Own and deliver multiple and complex analytic projects. This would require an understanding of business context, conversion of business problems in modeling, and implementing such solutions to create economic value.
• Domain expert - Individuals are expected to be domain expert in their sub field, as well as have a holistic view of other business lines to create better solutions. Key fields of focus are new customer acquisition, existing customer management, customer retention, product development, pricing and payment optimization and digital journey.
• Modeling and Tech Savvy - Always up to date with the latest use cases of modeling community, machine learning and deep learning algorithms and share knowledge within the team.
• Statistical mind set - Proficiency in basic statistics, hypothesis testing, segmentation and predictive modeling.
• Communication skills - Ability to translate and articulate technical thoughts and ideas to a larger audience including influencing skills with peers and senior management.
• Strong project management skills.
• Ability to coach and mentor juniors.
• Contribute to organizational initiatives in wide ranging areas including competency development, training, organizational building activities etc.

Role Responsibilities:

• Incumbents are required to work with large and complex data sets using a variety of tools (Python, PySpark, SQL, R etc.) to build modeling solutions for various business requirements
• Primary focus areas would be Model building, model validations, model implementation and model governance related responsibilities for multiple portfolios
• Responsible for documenting data requirements, data collection / processing / cleaning, and exploratory data analysis; which may include utilizing statistical models / algorithms and data visualization techniques
• Incumbents in this role may often be referred to as Data Scientists
• Specialization in marketing, risk, digital and AML fields possible
• The analyst will work with other members in the team and business partners to jointly build model driven solutions using traditional methods (Linear, Logistic, Segmentation etc.) as well Machine Learning driven modeling solutions
• Work with model governance & fair lending teams to ensure compliance of models in accordance with Citi standards
• Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency
• Work with large and complex datasets using a variety of tools (Python, PySpark, SQL, Hive, etc.) and frameworks to build Deep learning/generative AI solutions for various business requirements.
• Primary focus areas include model training/fine-tuning, model validation, model deployment, and model governance related to multiple portfolios.
• Design, fine-tune and implement LLMs/GenAI applications using techniques like prompt engineering, Retrieval Augmented Generation (RAG) and model fine-tuning
• Responsible for documenting data requirements, data collection/processing/cleaning, and exploratory data analysis, including utilizing deep learning /generative AI algorithms and, data visualization techniques.
• Incumbents in this role may often be referred to as Data Scientists.
• Specialization in marketing, risk, digital, and AML fields possible, applying Deep learning & generative AI models to innovate in these domains.
• Collaborate with team members and business partners to build model-driven solutions using cutting-edge Generative AI models (e.g., Large Language Models) and also at times, ML/ traditional methods (XGBoost, Linear, Logistic, Segmentation, etc.)
• Work with model governance & fair lending teams to ensure compliance of models in accordance with Citi standards.
• Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules, and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency

What do we look for:

If you are a bright and talented individual looking for a career in AIM, Citi has amazing opportunities for you.

• Bachelor’s Degree with 3 years of experience in data analytics, or Master’s Degree with 2 years of experience in data analytics, or PhD.
• Technical Skills
  • Hands-on experience in PySpark/Python/R programing along with strong experience in SQL.
  • 2-4 years of experience working with machine learning and statistical modeling techniques.
  • Understanding around deep learning techniques.
  • Experience working with large and multiple datasets, data warehouses and ability to pull data using relevant programs and coding.
  • Strong background in Statistical Analysis.
  • Capability to validate/maintain deployed models in production
• Self-motivated and able to implement innovative solutions at fast pace
• Experience in Machine Learning software frameworks
• Experience in Credit Cards and Retail Banking is preferred
• Competencies
  • Strong communication skills
  • Multiple stake holder management
  • Strong analytical and problem solving skills
  • Excellent written and oral communication skills
  • Strong team player
  • Control orientated and Risk awareness
  • Working experience in a quantitative field
  • Willing to learn and can-do attitude
  • Ability to build partnerships with cross-function leaders

Education:

• Bachelor's / Master degree in Economics / Statistics / Mathematics / Information Technology / Computer Applications / Engineering etc. from a premier institute

Other Details

• Employment: Full Time
• Industry: Credit Cards, Retail Banking, Financial Services, Banking

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