Data Science Manager

Data Science Manager:

Primary Responsibilities:

• Manage a team of data scientists through predictive models and analytical exercises to deliver

business value, innovative approaches and quality execution.

• Design, Develop and Deploy advanced machine learning and Artificial Intelligence

algorithms/predictive models for use in Underwriting, Customer Management, Marketing, and

Operations;

• Functional lead and point of contact with business partners to support the needs and goals of all

Elevate portfolios, Rock teams, and Pods.

• Assess, clean, merge, and analyze large datasets adhering to standardized data manipulation

techniques and methodology by leveraging Python, Spark, Snowflakes, etc.

• Design, Develop and Deploy linear, nonlinear, and other ML algorithms for testing, development

and deployment into our underwriting engine in the application of risk management in all of

Elevates acquisition channels;

• Efficiently apply data mining methodologies to minimize credit/fraud losses, maximize response

and approval rates, and develop methods to enhance profitability of Elevate products;

• Successfully implement scoring models on multiple decision platforms Including cloud.
• Provide knowledge, insight and guidance of third party data providers such as Transunion,

Clarity/Experian and Equifax to include knowledge of products and data available, products to

purchase or discontinue, cost benefit analysis of retrospective analysis, effective use of variables,

data dictionaries as well as advantages and limitations;

• Maintain clear, detailed model documentation on our Wiki Server by leveraging reproducible

research technologies such as Jupyter Notebook, Rmarkdown, etc.;

Experience and Education:

Master’s degree in highly quantitative field (Statistics, Economics, Mathematics, Engineering, or

other quantitatively-oriented degree). Ph.D. preferred.

• At least six years of experience in Data Science or Modeling for consumer lending, two years of

leadership experience in highly quantitative teams; Professional experience waived with at least

four years of Data Science or Modeling experience and Ph.D. Degree in highly quantitative field

(Statistics, Economics, Mathematics, or other quantitatively oriented degree)

• Superior communication skills for communication with Risk Management peers and executive team
• Proven experience working in fast-paced environment with ever-changing demands
• Demonstrated proficiency with advanced statistical modeling and substantial experience with

machine learning techniques (e.g., Random Forest, Gradient Boosting, LASSO, Elastic Net, etc).

Knowledge various penalized regression and classification methods a plus;

• Strong data skills, with ability to conduct substantial data munging/engineering.
• Proficiency with Linux, R, Python, or Java; expertise with versioning software (e.g., Git), big data

solutions and data processing frameworks (e.g., Spark, Hadoop);

• Experience with database technologies such as Snowflakes, Hadoop, Apache Hive/Impala, Spark,

NoSQL, JSON & XML parsing, etc.

• Proficiency of contemporary supervised and unsupervised data mining techniques a plus

Required Skills, Abilities, Soft Skill Factors

• Motivation Skills - History of achieving aggressive organizational goals and objectives, conveying

sense of urgency while moving beyond challenges and obstacles.

• Solid Analytical and Problem-Solving Skills - Ability to analyze trends and suggest solutions to

challenges.

• Achieve Successful Results – Takes the initiative to get things done.
• Demonstrates Adaptability – Works effectively in the face of stress, ambiguity, difficult situations

and shifting priorities

• Innovates – Challenges the status quo thinking to generate new ideas; takes open minded

approach to situations.

• Communication Skills - Refined written and verbal communication skills. Ability to foster open

communications, listen effectively and build strong partnership networks.

• Technological Competence – Extensive knowledge of R, Python, or other programming languages

and risk management technology with the ability to leverage such tools to improve the

organization’s decision-making criteria.