Data Scientist

About the role

A Senior Data Scientist I should be able to define the scope of a project with support of managers and execute that project independently. Individuals in this role can also support the development and training of junior staff. A Senior Data Scientist I should be self-sufficient in executing basic methods, and work within their teams to execute increasingly sophisticated approaches to deliver outcomes. They should also support the development of best practices.

Responsibilities

• Developing, analyzing, and modeling operational, economic, management, accounting and other organizational data to quantify the competitive performance of business segments, evaluate potential operational changes, and design new approaches and methodologies
• Analyzing organizational data to recommend solutions to new and complex problems, developing innovative strategies, quantifying the competitive performance of the organization's operations and/or markets; modeling and evaluating the potential impact of changes
• Applying and integrating statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex high-volume data from a variety of sources

Requirements

• Bachelor’s degree in Mathematics, Statistics, Computer Science, Data Science, or other quantitative discipline (or equivalent years of experience); Master’s/Ph.D. degree preferred.
• Actuarial experience/certification also preferred.
• 3+ years demonstrated experience in data manipulation and various AI/ML methodologies, preferably in applications using credit data for insurance or financial services
• Strong expertise in one or more of the following: R, Python, SQL, or equivalent analytic software
• Strong programming skills in Python and/or R, with extensive experience with their standard data manipulation and ML packages (pandas, scikit-learn, NumPy, XGBoost, PyTorch in Python and rpart, party, caret in R) and/or Scala
• Strong ability as a self-starter to learn new technologies (Pyspark, ECL, Azure/AWS ML Services) and to share cross-functional knowledge across the teams