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Data Scientist Assistant Vice PresidentCiti • United States
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Data Scientist Assistant Vice President

Citi
  • US
    United States
  • US
    United States
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About

Data Scientist Assistant Vice President
We are seeking a highly skilled and motivated Data Scientist at the Assistant Vice President level to join our dynamic team. This role is pivotal in driving data-driven decision-making and innovation within Citi. The ideal candidate will leverage advanced machine learning (ML) techniques and robust data science capabilities to solve complex business problems, uncover insights, and build scalable solutions that create significant value for the firm and its clients. You will be responsible for the end-to-end lifecycle of data science projects, from conceptualization and data exploration to model development, deployment, and performance monitoring. This position requires a deep technical expertise in Python, machine learning, and statistical analysis, combined with strong business acumen and the ability to communicate complex findings to both technical and non-technical stakeholders. Key Responsibilities Model Development & Implementation: Design, develop, and implement sophisticated machine learning models (e.g., for forecasting, classification, clustering, anomaly detection, and natural language processing) using Python and its core data science libraries (scikit-learn, TensorFlow, PyTorch, Keras). End-to-End Project Ownership: Lead all phases of the data science project lifecycle, including requirements gathering, data sourcing and wrangling, feature engineering, model training and validation, and deployment into production environments. Data Analysis & Insights: Perform exploratory data analysis on large, complex datasets to identify trends, patterns, and actionable insights that address key business questions. Technical Leadership & Mentoring: Provide technical guidance to junior data scientists and analysts. Champion best practices in coding, data science methodologies, and model validation. Stakeholder Collaboration: Partner with business leaders, product managers, and technology teams to understand challenges, define project objectives, and translate business needs into quantitative analyses and predictive models. Communication: Clearly and effectively present complex analytical concepts, methodologies, and results to diverse audiences, including senior management. Innovation & Strategy: Stay current with the latest advancements in the fields of machine learning and artificial intelligence. Proactively identify opportunities to apply novel techniques to drive competitive advantage and operational efficiency. Governance & Compliance: Ensure all models and data processes adhere to internal risk management policies, regulatory requirements, and model governance standards. Qualifications & Skills Essential: Experience: 5-7+ years of hands-on experience in a data science or quantitative role, with a proven track record of developing and deploying machine learning models in a corporate environment. Programming Proficiency: Expert-level programming skills in Python, including extensive experience with data manipulation (pandas, NumPy), machine learning (scikit-learn, XGBoost), and deep learning (TensorFlow, PyTorch) libraries. Database Skills: Strong proficiency in SQL for querying and extracting data from relational databases (e.g., Oracle, PostgreSQL). Machine Learning Expertise: Deep understanding of both classical machine learning algorithms (e.g., Linear/Logistic Regression, Random Forests, Gradient Boosting) and modern deep learning architectures (e.g., CNNs, RNNs, Transformers). Statistical Knowledge: Solid foundation in statistics, probability, and experimental design. Problem-Solving: Exceptional analytical and problem-solving skills with the ability to break down complex problems into manageable components. Communication: Excellent verbal and written communication skills, with the ability to articulate technical topics to non-technical audiences. Educational Background: A Bachelor's degree in a similar field with significant, equivalent professional experience will also be considered. Preferred: Financial Services Experience: Prior experience working in the financial industry (e.g., banking, risk management, algorithmic trading, fraud detection) is highly desirable. Full-Stack Capability: Experience or familiarity with full-stack development, including backend technologies like Java and frontend frameworks like Angular, is a plus. Big Data Technologies: Experience with distributed computing frameworks like Apache Spark (PySpark) and the Hadoop ecosystem. Cloud Platforms: Hands-on experience with data science and ML services on cloud platforms such as AWS (Sagemaker), Google Cloud (AI Platform), or Azure (Machine Learning). MLOps: Familiarity with MLOps principles and tools for model versioning (Git), containerization (Docker, Kubernetes), and CI/CD pipelines. Data Visualization: Proficiency with data visualization tools and libraries.
  • United States

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