Data Scientist-Mid Level
We are seeking a hardworking and dedicated Data Scientist-Mid Level to work remotely and convene with team members at least 4 times per year.
Uses sophisticated techniques that integrate traditional and non-traditional datasets and method to enable analytical solutions. Applies predictive analytics, machine learning, simulation, and optimization techniques to generate management insights and enable customer-facing applications; participates in building analytical solutions maximizing internal and external applications to deliver value and build competitive advantage. Translates sophisticated analytical and technical concepts to non-technical employees.
Job Requirements
Tasks:
Identifies and leads existing and emerging risks that stem from business activities and the job role.
Ensures risks associated with business activities are successfully identified, measured, supervised, and controlled.
Follows written risk and compliance policies, standards, and procedures for business activities.
Partners with analysts across the organization to fully define business problems and research questions; Supports SMEs on cross matrixed teams to take on highly sophisticated work critical to the organization.
Integrates and extracts relevant information from large amounts of both structured and unstructured data (internal and external) to enable analytical solutions.
Conducts sophisticated analytics using predictive modeling, machine learning, simulation, optimization and other techniques to deliver insights or develop analytical solutions to achieve business objectives.
Supports Subject Matter Experts (SME's) on efforts to develop scalable, efficient, automated solutions for large scale data analyses, model development, model validation and model implementation.
Works with IT to research architecture for new products, services, and features.
Develops algorithms and supporting code such that research efforts are based on the highest quality data.
Translates sophisticated analytical and technical concepts to non-technical employees to enable understanding and drive advised business decisions.
Minimum Requirements:
Master's degree in Computer Science, Applied Mathematics, Quantitative Economics, Statistics, or related field; OR 6 years of related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree.
4 years of related experience in predictive modeling, large data analysis and computer science.
Proficient knowledge of the function/discipline and proven application of knowledge, skills, and abilities towards work products.
Proficient level of business insight in the areas of the business operations, industry practices and emerging trends.
Experience in data mining and statistical analysis.
Knowledge of Data Science principals and experience with data science methodologies.
Experience with any one of the following statistical and predictive modeling approaches: Gaussian Process; Markov Models; Hierarchical Clustering; K-Means; Linear Regression; Logistic Regression; Monte Carlo Simulation; Neural Networks.
Preferred Experience:
Relevant banking domain knowledge particularly in areas such as AML, Fraud, Consumer Disputes, or Central Operations.
Strong Python, SQL skills.
Project experience with Graph Databases such as Neo4j, AWS Neptune or similar.
Worked with data platforms such as Hive in Hadoop, Spark, Snowflake.
Exposure to natural language processing (NLP), deep learning, computer vision.
Model risk experience, or hands-on experience taking models through development and model risk validation, at a financial institution guided by OCC 2011-12 and FRB SR 11-7.
Proven ability to effectively communicate (written and oral) complex analytical and technical concepts to both technical and non-technical employees.
Apply Now
Uses sophisticated techniques that integrate traditional and non-traditional datasets and method to enable analytical solutions. Applies predictive analytics, machine learning, simulation, and optimization techniques to generate management insights and enable customer-facing applications; participates in building analytical solutions maximizing internal and external applications to deliver value and build competitive advantage. Translates sophisticated analytical and technical concepts to non-technical employees.
Job Requirements
Tasks:
Identifies and leads existing and emerging risks that stem from business activities and the job role.
Ensures risks associated with business activities are successfully identified, measured, supervised, and controlled.
Follows written risk and compliance policies, standards, and procedures for business activities.
Partners with analysts across the organization to fully define business problems and research questions; Supports SMEs on cross matrixed teams to take on highly sophisticated work critical to the organization.
Integrates and extracts relevant information from large amounts of both structured and unstructured data (internal and external) to enable analytical solutions.
Conducts sophisticated analytics using predictive modeling, machine learning, simulation, optimization and other techniques to deliver insights or develop analytical solutions to achieve business objectives.
Supports Subject Matter Experts (SME's) on efforts to develop scalable, efficient, automated solutions for large scale data analyses, model development, model validation and model implementation.
Works with IT to research architecture for new products, services, and features.
Develops algorithms and supporting code such that research efforts are based on the highest quality data.
Translates sophisticated analytical and technical concepts to non-technical employees to enable understanding and drive advised business decisions.
Minimum Requirements:
Master's degree in Computer Science, Applied Mathematics, Quantitative Economics, Statistics, or related field; OR 6 years of related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree.
4 years of related experience in predictive modeling, large data analysis and computer science.
Proficient knowledge of the function/discipline and proven application of knowledge, skills, and abilities towards work products.
Proficient level of business insight in the areas of the business operations, industry practices and emerging trends.
Experience in data mining and statistical analysis.
Knowledge of Data Science principals and experience with data science methodologies.
Experience with any one of the following statistical and predictive modeling approaches: Gaussian Process; Markov Models; Hierarchical Clustering; K-Means; Linear Regression; Logistic Regression; Monte Carlo Simulation; Neural Networks.
Preferred Experience:
Relevant banking domain knowledge particularly in areas such as AML, Fraud, Consumer Disputes, or Central Operations.
Strong Python, SQL skills.
Project experience with Graph Databases such as Neo4j, AWS Neptune or similar.
Worked with data platforms such as Hive in Hadoop, Spark, Snowflake.
Exposure to natural language processing (NLP), deep learning, computer vision.
Model risk experience, or hands-on experience taking models through development and model risk validation, at a financial institution guided by OCC 2011-12 and FRB SR 11-7.
Proven ability to effectively communicate (written and oral) complex analytical and technical concepts to both technical and non-technical employees.
Apply Now