Specialized Analytics Sr Anlst - C12 - GURUGRAM

Control Automation Data Science Lead (C12) - DS

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.

About AIM: 

Analytics and Information Management (AIM) is a global community that is driving data driven transformation across Citi in multiple functions with the objective to create actionable intelligence for our business leaders. We are a fast-growing organization working with Citi businesses and functions across the world. 

What do we do:

We have one formula for managing risk across the firm: our Enterprise Risk Management Framework. This consistency enables us to take an end-to-end view in how we identify, measure, build, control and report risks. We are also working to simplify, streamline and automate our manual controls—strengthening our ability to prevent issues, not just to detect them after they happen. When issues do appear, we should strive to understand the root cause. We can then apply those lessons-learned horizontally across the organization to prevent similar issues from arising elsewhere.

Roles and Responsibilities:

• Manage a team of data scientists, support and provide technical guidance.

• Provide quantitative and qualitative insights on user experience and data sets performance.

• Work with cross-functional teams and leaders within the organization on modelling for critical business decisions

• Identify new opportunities for leveraging data science to improve the operational efficiency of the organization.

• Assimilate and incorporate business requirements and technical aspects.

• Assist data architect and data analyst in data gathering techniques and design processes for maintenance of databases, data accuracy and integrity.

• Ensure high data integrity, choosing the best tools and techniques for algorithms, frameworks, and technologies.

• Design and develop a new process for data modelling and production, and help businesses create and track their key performance metrics.

• Partner with other multi-functional teams, such as Engineering, Product Management, and Design, to collaborate on projects.

• Set goals and metrics and define KPIs to measure and track the improvement of the data analytics process.

• Analyze the data modelling, revenue generation, and targeting.

Skills and attributes for success

• Demonstrated experience working on complex Data Science projects.

• Knowledge and experience of all phases of SDLC (including requirements analysis, design, coding, testing, defect management, and deployment)

• Good understanding ofBanking domain (Wealth, Cards, Deposit, Loans & Insurance etc.)

• Knowledge of Finance Regulations, Understanding of Audit Process

• Exposure to business intelligence, data warehousing/platform, and data strategy projects

• Exceptional communication skills and stakeholder management experiences

• Ability to thrive in a dynamic and fast-paced environment.

Qualifications:

• BSc and MSc or PhD in Computer Science, Statistics, Mathematics, Engineering, Physics or related science field from a well-established University

• Minimum of 8-12 years of proven working experience in Data Science & ML areas, leading a team of 5-8 people

• Solid theoretical and hands-on knowledge of Econometrics, Statistics, Data Mining, Machine Learning & AI concepts

• Exceptional programming skills in Data Science scripting (such as Python / R)

• Deep Learning concepts & tools (H2O, TensorFlow, PyTorch, Keras, OpenAI and LangChain

etc.)

• Experience in SQL and interpretation of large databases (via tools such as Hive, Spark)

• Exceptional analytical and critical thinking skills with demonstrated ability to think strategically; turn consumer behavior data into effective strategies and drive results.

• Ability to communicate results effectively and explain technical methodologies in non-technical audience.

• Considered a plus (not a prerequisite):

• Experience with Cloud Technologies (MS Azure, GCP, AWS)

• Understanding of ML lifecycle and hands-on experience with ML Ops

• Agentic AI

• Visualization tools (Power BI, Tableau)