Data Scientist Assistant Vice President

Citi • Tampa, FL

11d • $96,960 - $145,440 • Onsite

About The Position

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.

Requirements

• 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.
• 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.
• Strong proficiency in SQL for querying and extracting data from relational databases (e.g., Oracle, PostgreSQL).
• 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).
• Solid foundation in statistics, probability, and experimental design.
• Exceptional analytical and problem-solving skills with the ability to break down complex problems into manageable components.
• Excellent verbal and written communication skills, with the ability to articulate technical topics to non-technical audiences.
• A Bachelor's degree in a similar field with significant, equivalent professional experience will also be considered.

Nice To Haves

• Prior experience working in the financial industry (e.g., banking, risk management, algorithmic trading, fraud detection) is highly desirable.
• Experience or familiarity with full-stack development, including backend technologies like Java and frontend frameworks like Angular, is a plus.
• Experience with distributed computing frameworks like Apache Spark (PySpark) and the Hadoop ecosystem.
• Hands-on experience with data science and ML services on cloud platforms such as AWS (Sagemaker), Google Cloud (AI Platform), or Azure (Machine Learning).
• Familiarity with MLOps principles and tools for model versioning (Git), containerization (Docker, Kubernetes), and CI/CD pipelines.
• Proficiency with data visualization tools and libraries.

Responsibilities

• 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).
• 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.
• Perform exploratory data analysis on large, complex datasets to identify trends, patterns, and actionable insights that address key business questions.
• Provide technical guidance to junior data scientists and analysts. Champion best practices in coding, data science methodologies, and model validation.
• 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.
• Clearly and effectively present complex analytical concepts, methodologies, and results to diverse audiences, including senior management.
• 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.
• Ensure all models and data processes adhere to internal risk management policies, regulatory requirements, and model governance standards.

Benefits

• medical, dental & vision coverage
• 401(k)
• life, accident, and disability insurance
• wellness programs
• paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level