Senior Data Scientist – Model Development

Role Overview

• Gathers, interprets, and manipulates structured and unstructured data to enable advanced analytical solutions for the business.
• Develops scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
• Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Composes, and assists peers with composing, technical documents for knowledge persistence, risk management, and technical review audiences.
• Assesses business needs to propose/recommend analytical and modeling projects to add business value.
• Works with business and analytics leaders to prioritize analytics and modeling problems/research efforts.
• Builds and maintains a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data.
• Translates complex business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations.
• Manages project milestones, risks, and impediments.
• Escalates potential issues that could limit project success or implementation.
• Develops best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards.
• Maintains expertise and awareness of cutting-edge techniques.
• Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.
• Serves as a mentor to junior data scientists in modeling, analytics, and computer science tasks.
• Participates in internal communities that drive the maintenance and transformation of data science technologies and culture.
• Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.

Requirements

• Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree.
• 6 years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master’s, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline and 4 years of experience in predictive analytics or data analysis.
• 4 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
• 4 years of experience in one or more dynamic scripted language (such as Python, R, etc.) for performing statistical analyses and/or building and scoring AI/ML models.
• Proven experience writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
• Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
• Strong experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.
• Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
• Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
• Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc.
• Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
• Experience guiding and mentoring junior technical staff in business interactions and model building.
• Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.

Tech Stack

• NoSQL
• Python
• SQL

Benefits

• comprehensive medical, dental and vision plans
• 401(k)
• pension
• life insurance
• parental benefits
• adoption assistance
• paid time off program with paid holidays plus 16 paid volunteer hours
• various wellness programs
• career path planning and continuing education