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
Translate 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
Ensure 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 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

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningpredictive analyticsdata analysisstatistical modelingPythonRSQLHQLNoSQLdata preprocessing

Soft Skills

communicationmentoringproject managementproblem-solvingcollaborationanalytical thinkingrisk managementbusiness acumentechnical writingadaptability