Data Scientist

The Data Scientist provides advanced analytics, modeling, and data-driven decision support across enterprise functions. This role includes designing and developing machine learning models, performing statistical analysis, and collaborating with partners to frame problems, gather relevant data, and translate insights into strategic business recommendations. The Data Scientist ensures model development and deployment aligns with enterprise governance standards and drives measurable impact across key performance areas., Translates complex business problems into analytical solutions using machine learning, statistical modeling, and AI techniques

Designs, develops, and evaluates models and algorithms for classification, prediction, clustering, and optimization

Works closely with data engineers and platform administrators to ensure proper data pipelines, infrastructure readiness, and scalable solutions

Communicates findings and insights clearly to both technical and non-technical stakeholders

Adheres to enterprise model governance standards, including documentation, validation, and risk assessment

Partners with business and technical teams to ensure models are continuously monitored and updated as needed

Utilizes structured and unstructured data from various systems (internal and external) to enhance model accuracy and performance

Stays current on emerging techniques and tools in data science and applies them where appropriate

Strong statistical foundation and experience in developing, validating, and deploying predictive models

Experience with supervised and unsupervised learning, natural language processing, and time series analysis

Proficiency in Python for data science applications, along with experience in libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, pandas, etc.

Experience with SQL and working knowledge of data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn)

Familiarity with cloud environments (e.g., Azure, AWS, or GCP) and model deployment strategies (e.g., Docker, APIs, MLOps pipelines)

Understanding of ethical AI practices, data privacy, and model interpretability

Strong problem-solving skills and ability to manage ambiguity in data and requirements

Ability to manage multiple projects simultaneously and adapt in a fast-paced environment Desired Qualifications

Domain experience in financial services

Exposure to model risk governance frameworks (e.g., SR11-7)

Experience contributing to or using reusable modeling frameworks or shared libraries

Prior involvement in AI/ML innovation labs, research, or open-source contributions