Data Analytics Lead - Data Scientist - Vice President

Citi•Indianapolis, IN

21h

About The Position

The Data Analytics Lead / Data Scientist is a strategic professional who stays abreast of developments within own field and contributes to directional strategy by considering their application in own job and the business. Recognized technical authority for an area within the business. Requires basic commercial awareness. There are typically multiple people within the business that provide the same level of subject matter expertise. Developed communication and diplomacy skills are required in order to guide, influence and convince others, in particular colleagues in other areas and occasional external customers. Significant impact on the area through complex deliverables. Provides advice and counsel related to the technology or operations of the business. Work impacts an entire area, which eventually affects the overall performance and effectiveness of the sub-function/job family. Responsibilities: Integrates subject matter and industry expertise within a defined area. Contributes to data analytics standards around which others will operate. Applies in-depth understanding of how data analytics collectively integrate within the sub-function as well as coordinate and contribute to the objectives of the entire function. Employs developed communication and diplomacy skills are required in order to guide, influence and convince others, in particular colleagues in other areas and occasional external customers. Resolves occasionally complex and highly variable issues. Produces detailed analysis of issues where the best course of action is not evident from the information available, but actions must be recommended/ taken. Responsible for volume, quality, timeliness and delivery of data science projects along with short-term planning resource planning. 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. Lead the design and execution of complex data analysis and AI/ML initiatives across large, structured, and unstructured datasets. Develop and deploy predictive, classification, clustering, and forecasting models to support business strategy and risk management. Partner with business stakeholders to translate requirements into analytical and machine learning solutions. Design and implement feature engineering pipelines and model evaluation frameworks. Collaborate with Data Engineering teams to ensure scalable data pipelines and ML-ready datasets. Operationalize machine learning models through production deployment and monitoring (MLOps practices). Analyze trends, anomalies, and behavioral patterns using statistical and machine learning techniques. Ensure model governance, explainability, fairness, and compliance with regulatory requirements. Automate analytics workflows and implement scalable AI-driven solutions. Present analytical findings and model insights to senior leadership and cross-functional teams. Mentor junior analysts and data scientists on advanced analytics and ML best practices. Drive continuous improvement in analytical methodologies, model performance, and reporting standards. Influence strategic decisions through data science and AI-powered insights. Manage multiple priorities in a fast-paced, highly regulated environment.

Requirements

10-15 years of relevant experience in Data Analytics, Data Science, or Advanced Analytics roles.
Advanced proficiency in SQL and relational database concepts.
Strong programming experience in Python (required); PySpark preferred.
Hands-on experience building and deploying machine learning models (supervised and unsupervised).
Experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
Strong knowledge of statistical modeling, feature engineering, and model validation techniques.
Experience with BI tools such as Tableau or Power BI.
Familiarity with MLOps practices (model deployment, monitoring, versioning) is strongly preferred.
Experience working with large-scale enterprise or financial datasets.
Understanding of data warehousing, ETL, and big data ecosystems.
Strong problem-solving, analytical thinking, and stakeholder management skills.
Proven ability to communicate complex AI/ML insights to non-technical audiences.
Experience in banking or financial services preferred.
Bachelor’s/University degree or equivalent experience, potentially Masters degree
Master’s degree or specialization in AI/ML/Data Science preferred.

Nice To Haves

PySpark preferred.
Familiarity with MLOps practices (model deployment, monitoring, versioning) is strongly preferred.
Experience in banking or financial services preferred.
Master’s degree or specialization in AI/ML/Data Science preferred.

Responsibilities

Integrates subject matter and industry expertise within a defined area.
Contributes to data analytics standards around which others will operate.
Applies in-depth understanding of how data analytics collectively integrate within the sub-function as well as coordinate and contribute to the objectives of the entire function.
Employs developed communication and diplomacy skills are required in order to guide, influence and convince others, in particular colleagues in other areas and occasional external customers.
Resolves occasionally complex and highly variable issues.
Produces detailed analysis of issues where the best course of action is not evident from the information available, but actions must be recommended/ taken.
Responsible for volume, quality, timeliness and delivery of data science projects along with short-term planning resource planning.
Lead the design and execution of complex data analysis and AI/ML initiatives across large, structured, and unstructured datasets.
Develop and deploy predictive, classification, clustering, and forecasting models to support business strategy and risk management.
Partner with business stakeholders to translate requirements into analytical and machine learning solutions.
Design and implement feature engineering pipelines and model evaluation frameworks.
Collaborate with Data Engineering teams to ensure scalable data pipelines and ML-ready datasets.
Operationalize machine learning models through production deployment and monitoring (MLOps practices).
Analyze trends, anomalies, and behavioral patterns using statistical and machine learning techniques.
Ensure model governance, explainability, fairness, and compliance with regulatory requirements.
Automate analytics workflows and implement scalable AI-driven solutions.
Present analytical findings and model insights to senior leadership and cross-functional teams.
Mentor junior analysts and data scientists on advanced analytics and ML best practices.
Drive continuous improvement in analytical methodologies, model performance, and reporting standards.
Influence strategic decisions through data science and AI-powered insights.
Manage multiple priorities in a fast-paced, highly regulated environment.