Data Scientist

Create, implement, and launch cutting-edge machine learning and AI-driven algorithms/predictive models to enhance Underwriting, Customer Management, Marketing, and Operational processes

Evaluate, preprocess, combine, and analyze extensive datasets using standardized data manipulation methods and techniques, utilizing tools like R, Python, and/or Apache Spark

Offer expertise and guidance on third-party data providers including knowledge of available products and data, recommendations on what to purchase or discontinue, cost-benefit analysis, data dictionaries, effective use of variables, and understanding their limitations and advantages

Create, build, and implement both linear and nonlinear algorithms for testing, development, and integration into our underwriting engine, focusing on risk management across all channels

Effectively utilize data mining techniques to optimize response and approval rates and develop strategies to boost the profitability of products

Ensure clear and detailed model documentation on Wiki Server using reproducible research tools like IPython, Rmarkdown, Jupyter Notebook, and others

Deploy scoring models on different platforms, including R, on-premise, and cloud systems, in formats like Java objects, R models, and Apache Spark models.

Serve as the primary point of contact and functional lead for business partners, ensuring support for all needs and objectives

Guide and oversee analytical projects while mentoring Junior Data Scientists



Experience and Education:

A Master's degree in a highly quantitative discipline (e.g. Economics, Statistics, Mathematics, Engineering, or similar fields)

Hands-on experience in Data Science or Modeling.

Competence in Linux and proficiency in R, Python, or Java; experience with version control tools (e.g., Git), as well as big data technologies and frameworks (e.g., Spark, Hadoop).

Proven expertise in advanced statistical modeling and substantial exposure to machine learning techniques such as Random Forest, LASSO, Gradient Boosting, Elastic Net, etc.

Proven ability to thrive in fast-paced environments with shifting priorities, while effectively communicating and collaborating with Risk Management teams and executives.

Strong data manipulation and engineering abilities, with experience in performing complex data transformations

Hands-on experience with a range of database technologies, including but not limited to MSSQL Server, SAS Datasets, Hadoop, Kafka, Spark, Redshift, HBASE, Spark Streaming, Oracle, Neo4j, Teradata, MySQL, Amazon AWS, DB2, Cassandra, PostgreSQL, Apache Hive/Impala, and NoSQL data formats (including XML & JSON ).



Required Skills, Abilities, Soft Skill Factors:

Technological Expertise - Comprehensive knowledge of Python, Scala, R, Java, SAS, SQL, MATLAB, and/or SPSS, along with risk management technologies, enabling the use of these tools to enhance organizational decision-making.

Motivational Abilities - Proven track record of meeting challenging organizational goals, instilling a sense of urgency, and overcoming obstacles to deliver results.

Analytical and Administrative Abilities - Strong problem-solving and analytical thinking skills, with the capacity to assess trends and recommend solutions to complex issues.

Communication Proficiency - Excellent verbal and written communication skills, with the ability to promote open dialogue, listen attentively, and cultivate strong professional relationships.

Innovative Thinking - Continuously challenges conventional approaches to foster new ideas and embraces a flexible mindset.

Adaptability - Demonstrates resilience and effectiveness when dealing with stress, uncertainty, difficult situations, and changing priorities.

Results-Oriented - Self-motivated and proactive in taking ownership of tasks and driving projects to completion.