David Bess

Technical Environment: Elastic (ELK), Apache Tika, Java, Angular 5, Spring, Docker, Jenkins, AWS Aurora Postgres, AWS ECS, AWS EC2, AWS Certificate Manager, CloudFormation, Google Search Appliance (GSA), Sharepoint 365, Drupal, OpenText.

Client launched an effort to modernize and improve their existing enterprise search solution. I lead the client through the process of a complete redesign and replatforming of their enterprise search solution. The search solution was replatformed from… Show more

Technical Environment: Elastic (ELK), Apache Tika, Java, Angular 5, Spring, Docker, Jenkins, AWS Aurora Postgres, AWS ECS, AWS EC2, AWS Certificate Manager, CloudFormation, Google Search Appliance (GSA), Sharepoint 365, Drupal, OpenText.

Client launched an effort to modernize and improve their existing enterprise search solution. I lead the client through the process of a complete redesign and replatforming of their enterprise search solution. The search solution was replatformed from google search appliance to Elastic (ELK). The new solution included all functionality available in the old solution as well as added new functionality.
• Lead solution architect responsible for creating and documenting all design aspects of the solution.
• Delivery Lead responsible for all aspects of project delivery and client communications.
• Technical Lead responsible for ensuring best practices are followed and quality standards are achieved by the entire delivery team.
• Software engineer designing and implementing new product features. Show less

Technical Environment: Cloudera (CDH 4 & CDH 5), MapReduce, Java, Scala, R, RStudio Server, Python, Jupyter Notebook, Hadoop Core, HDFS, Yarn, Hive, Dist CP, Spark 1.x, Spark 2.x, Hbase

Client's data science team wanted to incorporate big data into their existing work to improve model accuracy and discover previously hidden insights and patterns. Client had an existing data science team and an existing big data team in place, but the data science team had no prior experience working… Show more

Technical Environment: Cloudera (CDH 4 & CDH 5), MapReduce, Java, Scala, R, RStudio Server, Python, Jupyter Notebook, Hadoop Core, HDFS, Yarn, Hive, Dist CP, Spark 1.x, Spark 2.x, Hbase

Client's data science team wanted to incorporate big data into their existing work to improve model accuracy and discover previously hidden insights and patterns. Client had an existing data science team and an existing big data team in place, but the data science team had no prior experience working with big data, and the big data team did not have capacity to support the needs of the data science team. The data included semi-structured web logs and unstructured discussion posts. I was able to bridge the gap between the two teams and enable the data science team to derive value from big data sources. I also enabled self-service data extraction and analysis from Hadoop for the data science team by setting up RStudio Server and Jypyter Notebooks on a Hadoop edge node and providing training and documentation on how to use the solution.
• Lead solution architect responsible for creating and documenting all design aspects of the big data analytics solution.
• Delivery lead responsible for all aspects of project delivery and client communications.
• Data engineer hands on writing code to extract and transform big data into information assets which could be readily utilized by the analytics team.
• Requirements engineer working directly with stakeholders to define and refine functional requirements.
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Technical Environment: Cisco UCS C240, Linux, NIC Teaming & Bonding, SSD, NVIDIA, Cuda, GPUDirect, Docker, Kubernetes, TensorFlow, Theano, Torch, Java, Python (Jython), Spring, JQuery, Tomcat, Elastic (ELK), Deep Neural Network (DNN), Convolution Neural Network (CNN), Recursive Neural Network (RNN), Long Short Term Memory (LSTM) Neural Network.

CSAA introduced a new Automated Machine Learning (AML) and GPU Computing Platform in to their IT ecosystem in support of enabling AI and deep… Show more

Technical Environment: Cisco UCS C240, Linux, NIC Teaming & Bonding, SSD, NVIDIA, Cuda, GPUDirect, Docker, Kubernetes, TensorFlow, Theano, Torch, Java, Python (Jython), Spring, JQuery, Tomcat, Elastic (ELK), Deep Neural Network (DNN), Convolution Neural Network (CNN), Recursive Neural Network (RNN), Long Short Term Memory (LSTM) Neural Network.

CSAA introduced a new Automated Machine Learning (AML) and GPU Computing Platform in to their IT ecosystem in support of enabling AI and deep learning as well as batch and real time model scoring engines. Initial use cases were conducted on the platform to showcase its capabilities. The first big use case involves customers uploading pictures of vehicle damage through a mobile app and a deep convolution neural network would apply computer vision to identify the damaged areas of the vehicle. Deep neural networks were also used to provide an initial estimate of the vehicle repair costs.
• Data Science lead responsible for all feature engineering as well as all AI and deep learning model development.
• Supporting solution architect assisting to design and document aspects of the AML and GPU computing solution.
• System administrator working to do initial setup, installation, and configuration of the platform.
• Software engineer designing and implementing data pipelines, model scoring services, and backend components of web applications.
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Technical Environment: Amazon Alexa, Rasa.ai, Linux, NVIDIA, Cuda, GPUDirect, Docker, Kubernetes, Theano, Java, Python (Jython), Spring, JQuery, Tomcat, Elastic (ELK), Deep Neural Network (DNN), Recursive Neural Network (RNN), Long Short Term Memory (LSTM) Neural Network.

CSAA made a drive to establish new capabilities in the space of voice and chat to support the goal of staging perfect customer experiences, as well as increasing productivity and accessibility for employees.… Show more

Technical Environment: Amazon Alexa, Rasa.ai, Linux, NVIDIA, Cuda, GPUDirect, Docker, Kubernetes, Theano, Java, Python (Jython), Spring, JQuery, Tomcat, Elastic (ELK), Deep Neural Network (DNN), Recursive Neural Network (RNN), Long Short Term Memory (LSTM) Neural Network.

CSAA made a drive to establish new capabilities in the space of voice and chat to support the goal of staging perfect customer experiences, as well as increasing productivity and accessibility for employees. Applications included new customer facing chatbots as well as an Alexa Skills enabling customer self-service. Existing enterprise search solutions were made voice enabled.
• Data Science lead responsible for feature engineering and development of natural language processing and cognitive models.
• Supporting solution architect assisting to design and document aspects of the Chatbot and Voice solution.
• System administrator working to do initial setup, installation, and configuration of the Chatbot and Voice solution.
• Software engineer designing and implementing data pipelines and backend components of web applications.
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Technical Environment: Linux, Hadoop, Spark, Spark Streaming, Kafka, Scala, Java, Spring, Angular, Tomcat, Service Now, Data Power, Hbase, Elastic (ELK), Jenkins, Maven, SBT.

CSAA introduced an optional component of their property insurance products which allowed customers to opt into a home automation program. This effort was in support of defending and increasing market share by providing home automation options to potential and existing customers. Customers agree to share data from… Show more

Technical Environment: Linux, Hadoop, Spark, Spark Streaming, Kafka, Scala, Java, Spring, Angular, Tomcat, Service Now, Data Power, Hbase, Elastic (ELK), Jenkins, Maven, SBT.

CSAA introduced an optional component of their property insurance products which allowed customers to opt into a home automation program. This effort was in support of defending and increasing market share by providing home automation options to potential and existing customers. Customers agree to share data from their home automation devices, and in return they were given free Nest devices and access to a portfolio of value added services delivered through customer facing web portals and mobile applications. Data was ingested, stored, and processed within CSAAs IoT data platform.
• Lead solution architect responsible for creating and documenting all design aspects of the connected home solutions.
• Delivery Lead responsible for all aspects of project management and delivery.
• Software engineer designing and implementing data pipelines and backend components of web applications.
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Technical Environment: Linux, Docker, Kubernetes

CSAA introduced Docker and Kubernetes as a new technology in support of increased operational efficiency, lower IT incidents and outages, simply IT infrastructure sizing and planning, and lower IT infrastructure costs. A new Kubernetes cluster was design, developed and implemented. A portfolio of new and existing applications migrated from their current infrastructure (bare metal or VMware) to Docker and Kubernetes.
• Supporting… Show more

Technical Environment: Linux, Docker, Kubernetes

CSAA introduced Docker and Kubernetes as a new technology in support of increased operational efficiency, lower IT incidents and outages, simply IT infrastructure sizing and planning, and lower IT infrastructure costs. A new Kubernetes cluster was design, developed and implemented. A portfolio of new and existing applications migrated from their current infrastructure (bare metal or VMware) to Docker and Kubernetes.
• Supporting solution architect assisting to design and document aspects of the containerization solution.
• System administrator working to do initial setup, installation, and configuration of the Kubernetes platform.
• Software engineer writing code to containerize existing applications and deploy them on the Kubernetes platform.
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Technical Environment: Cisco UCS C240, Linux (RHEL), Hadoop, Spark, Spark Streaming, Kafka, Scala, Java, Spring, Angular, Tomcat, Service Now, Data Power, Hbase, Elastic (ELK), Jenkins, Maven, SBT, CAN bus, OBD-II, DTC.

CSAA introduced a new usage-based insurance product and a portfolio of telematics-based value-added services to defend their market share by providing an option for customers who are interested in such products and services. To support this effort a portfolio of IT… Show more

Technical Environment: Cisco UCS C240, Linux (RHEL), Hadoop, Spark, Spark Streaming, Kafka, Scala, Java, Spring, Angular, Tomcat, Service Now, Data Power, Hbase, Elastic (ELK), Jenkins, Maven, SBT, CAN bus, OBD-II, DTC.

CSAA introduced a new usage-based insurance product and a portfolio of telematics-based value-added services to defend their market share by providing an option for customers who are interested in such products and services. To support this effort a portfolio of IT applications, platforms, integrations, and other services were designed, developed, and deployed into production. Insurance Telematics IT Services included (1) a new IoT Data Platform, (2) a new telematics underwriting and device fulfillment platform, (3) a portfolio of new internal web applications, and (4) a portfolio of customer facing web portals and mobile applications.
• Lead solution architect responsible for creating and documenting all design aspects of the insurance telematics solution.
• Delivery Lead responsible for all aspects of project management and delivery.
• Software engineer designing and implementing data pipelines and backend components of web applications.
• System administrator working to do initial setup, installation, and configuration of the solution.
• Architecture and Engineering Leader contributing to strategic vision and roadmaps, budget planning and analysis, sizing and estimation of efforts, build vs buy decisions and vendor management, team building and mentoring, and hiring decision.
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Technical Environment: Linux, Hadoop, Spark, Spark Streaming, Speak ML, R, RStudio Server, Python, Kafka, Scala, Java, Spring, Angular, Tomcat, Service Now, DataPower, Hbase, Elastic (ELK), Jenkins, Redis, Maven, SBT, CAN bus, OBD-II, DTC.

CSAA created a driver behavior scoring model for their usage base insurance product offering. The model was used to measure risk associated with driving behaviors. A scoring model was implemented which translated directly to customer discounts and… Show more

Technical Environment: Linux, Hadoop, Spark, Spark Streaming, Speak ML, R, RStudio Server, Python, Kafka, Scala, Java, Spring, Angular, Tomcat, Service Now, DataPower, Hbase, Elastic (ELK), Jenkins, Redis, Maven, SBT, CAN bus, OBD-II, DTC.

CSAA created a driver behavior scoring model for their usage base insurance product offering. The model was used to measure risk associated with driving behaviors. A scoring model was implemented which translated directly to customer discounts and surcharges. A customer portal solution provided trip information and driver behavior feedback directly to the customer. The portal included showing the trip on a map and showed pin drops flagging the locations and times of risky driving behavior, along with tips on how to avoid such behavior.
• Data Science lead responsible for feature engineering and driver behavior and risk model development.
• Lead solution architect responsible for creating and documenting all design aspects of the solution.
• Delivery Lead responsible for all aspects of project management and delivery.
• Software engineer designing and implementing data pipelines, model scoring engine, and backend components of web applications.
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Technical Environment: UI Path, VMware, Windows Server, Data Power, Java, Spring, Elastic (ELK).

CSAA implemented a portfolio of new RPA IT services in support of improving operational efficiency, lowering workforce expense, and improving accuracy within business processes. Repeatable manual back office processes were automated using bots.
• Supporting solution architect assisting to design and document aspects of the RPA solution.
• System administrator working to do initial… Show more

Technical Environment: UI Path, VMware, Windows Server, Data Power, Java, Spring, Elastic (ELK).

CSAA implemented a portfolio of new RPA IT services in support of improving operational efficiency, lowering workforce expense, and improving accuracy within business processes. Repeatable manual back office processes were automated using bots.
• Supporting solution architect assisting to design and document aspects of the RPA solution.
• System administrator working to do initial setup, installation, and configuration of the RPA platform.
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Technical Environment: Cisco UCS C240, Linux, Hadoop, Hive, Pig, Python, Storm, Java UDF, Java, Spring, Tomcat, Avaya, Elastic (ELK).

CSAA implemented a new call center quality assurance IT solution based on transcribing all audio recording to text, conducting sentiment analysis, and exposing the results through a customer web application which enabled full text search supported by Elastic (ELK). The project supported improvement in customer net promoter score and improved the… Show more

Technical Environment: Cisco UCS C240, Linux, Hadoop, Hive, Pig, Python, Storm, Java UDF, Java, Spring, Tomcat, Avaya, Elastic (ELK).

CSAA implemented a new call center quality assurance IT solution based on transcribing all audio recording to text, conducting sentiment analysis, and exposing the results through a customer web application which enabled full text search supported by Elastic (ELK). The project supported improvement in customer net promoter score and improved the efficiency and accuracy of the call center quality assurance team. All PII and PCI information was obfuscating during the transcription process.
• Data Science lead responsible for feature engineering and development of the sentiment analysis model.
• Supporting solution architect assisting to design and document aspects of the solution.
• System integrator working to do initial setup, installation, and configuration of the voice transcription and indexing solution.
• Software engineer designing and implementing data pipelines and backend components of web applications.
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Technical Environment: Cisco UCS C240, Linux, Hadoop, Hive, Pig, Flume, Storm, MapReduce, Java, Java UDF, Golden Gate, Axway, HUE, Oracle DB, MS SQL Server, DB2, AS400.

CSAA implemented a new data lake based on big data technology in support of lowering infrastructure and storage costs, improving ETL runtimes, integrating disparate data silos, and unlocking new capabilities in real time and unstructured data processing and analytics. All enterprise data assets were captured and stored… Show more

Technical Environment: Cisco UCS C240, Linux, Hadoop, Hive, Pig, Flume, Storm, MapReduce, Java, Java UDF, Golden Gate, Axway, HUE, Oracle DB, MS SQL Server, DB2, AS400.

CSAA implemented a new data lake based on big data technology in support of lowering infrastructure and storage costs, improving ETL runtimes, integrating disparate data silos, and unlocking new capabilities in real time and unstructured data processing and analytics. All enterprise data assets were captured and stored in a single Hadoop based environment. Relational databases underwent full change data capture replication, and other sources, such as mainframe also had processes to capture changes.

• Supporting solution architect assisting to design and document aspects of the data lake solution.
• System administrator working to do initial setup, installation, and configuration of the Hadoop cluster.
• Software engineer designing and implementing new data pipelines and data services.
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Technical Environment: VMware, Linux, Neo4J, Python, Java, Spring, Tomcat, R, RStudio Server, Collaborative Filtering, PageRank.

CSAA introduced a new graph database platform and a portfolio of associated reports and applications in support specialized use cases around data exploration, data visualization, and anomaly detection. Example use cases included a claim fraud ring detection model and application, as well as a data exploration application supporting the claims special… Show more

Technical Environment: VMware, Linux, Neo4J, Python, Java, Spring, Tomcat, R, RStudio Server, Collaborative Filtering, PageRank.

CSAA introduced a new graph database platform and a portfolio of associated reports and applications in support specialized use cases around data exploration, data visualization, and anomaly detection. Example use cases included a claim fraud ring detection model and application, as well as a data exploration application supporting the claims special investigation unit.
• Data Science lead responsible for feature engineering and community detection model development.
• Supporting solution architect assisting to design and document aspects of the solution.
• System administrator working to do initial setup, installation, and configuration of the graph database platform.
• Software engineer designing and implementing data pipelines and backend components of web applications.
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CSAA replatformed a portfolio of predictive models and associated reports from a legacy platform based on SAS and Excel to a Big Data Platform in support of automating end to end operations including data extraction, model scoring, and report delivery. The project also enabled new data assets to be used within the models, enabled the modeling team to use new toolsets and have access to greater computing resources. New reporting capabilities not possible in excel where achieved through a… Show more

CSAA replatformed a portfolio of predictive models and associated reports from a legacy platform based on SAS and Excel to a Big Data Platform in support of automating end to end operations including data extraction, model scoring, and report delivery. The project also enabled new data assets to be used within the models, enabled the modeling team to use new toolsets and have access to greater computing resources. New reporting capabilities not possible in excel where achieved through a combination of Tableau reports and dashboard as well as custom built web applications. This transformation enabled the data science team for focus on model development while allowing IT to manage data extraction, operations, security, governance, and reporting aspects of the data science lifecycle.
• Data Science lead responsible for translating models from SAS to R and making improvements to the model during the transition process.
• Lead solution architect responsible for creating and documenting all design aspects of the solution.
• Delivery Lead responsible for all aspects of project management and delivery.
• System administrator working to do initial setup, installation, and configuration of the RStudio and Tableau platforms.
• Software engineer designing and implementing data pipelines, model scoring solutions, and backend components of web applications. Report developer implementing reporting solutions in Tableau.
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Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, DNN (Multilayer Perceptron), K-Means, Oversampling, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation.

CSAA developed a predictive model to identify fraudulent claims and created a portfolio of associated reports in support of cost reduction. Preform deep dive adhoc analysis to answer business questions related to claims fraud. Use control groups, A/B… Show more

Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, DNN (Multilayer Perceptron), K-Means, Oversampling, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation.

CSAA developed a predictive model to identify fraudulent claims and created a portfolio of associated reports in support of cost reduction. Preform deep dive adhoc analysis to answer business questions related to claims fraud. Use control groups, A/B testing, and optimization to quantify uplift achieved from working the model and tune the model over time.
• Supporting Data Scientist and report developer.
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Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, Gradient Boosting Machine, K-Means, Oversampling, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation.

CSAA developed a model to predict customer NPS for a claim early in the claims lifecycle and created a portfolio of associated reports in support of improving customer experience and customer service quality assurance. The model would detect early in the… Show more

Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, Gradient Boosting Machine, K-Means, Oversampling, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation.

CSAA developed a model to predict customer NPS for a claim early in the claims lifecycle and created a portfolio of associated reports in support of improving customer experience and customer service quality assurance. The model would detect early in the claims lifecycle if the claim was at risk of ultimately generating an unfavorable NPS survey result and notify a claims supervisor. Deep dive adhoc analysis was used to answer business questions related to claims NPS. Control groups, A/B testing, and optimization was used to quantify uplift achieved from working the model and tune the model over time.
• Supporting Data Scientist and report developer. Show less

Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, K-Means, Oversampling, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Convex Optimization.

CSAA developed a prescriptive model to prescribe if a vehicle should be totaled and salvaged or repaired and created a portfolio of associated reports in support of improving financial performance. Some vehicles can be salvaged for more money than their blue book… Show more

Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, K-Means, Oversampling, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Convex Optimization.

CSAA developed a prescriptive model to prescribe if a vehicle should be totaled and salvaged or repaired and created a portfolio of associated reports in support of improving financial performance. Some vehicles can be salvaged for more money than their blue book value. Preform deep dive adhoc analysis to answer business questions related to claims vehicle salvage and total loss. Use control groups, A/B testing, and optimization to quantify uplift achieved from working the model and tune the model over time.
• Supporting Data Scientist and report developer. Show less

Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, DNN (Multilayer Perceptron), K-Means, Oversampling, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation.

CSAA developed a predictive model to identify lost subrogation opportunities and created a portfolio of associated reports in support of cost reduction. Preform deep dive adhoc analysis to answer business questions related to claims subrogation. Use… Show more

Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, DNN (Multilayer Perceptron), K-Means, Oversampling, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation.

CSAA developed a predictive model to identify lost subrogation opportunities and created a portfolio of associated reports in support of cost reduction. Preform deep dive adhoc analysis to answer business questions related to claims subrogation. Use control groups, A/B testing, and optimization to quantify uplift achieved from working the model and tune the model over time.
• Supporting Data Scientist and report developer.
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Technical Environment: AIX, SAS, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, Random Forest, K-Means, Oversampling, Stratified Random Sampling, Longitudinal Studies, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Out of Time Validation, Linear Programming & Convex Optimization.

CSAA modeled future customer lifetime value and created a portfolio of associated reports in support of customer segmentation, product management decision… Show more

Technical Environment: AIX, SAS, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, Random Forest, K-Means, Oversampling, Stratified Random Sampling, Longitudinal Studies, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Out of Time Validation, Linear Programming & Convex Optimization.

CSAA modeled future customer lifetime value and created a portfolio of associated reports in support of customer segmentation, product management decision science, forecasting, and portfolio insights. Deep dive adhoc analysis was used to answer business questions related to customer churn, revenue, and cost by customer segment. Control groups, A/B testing, and optimization was used to quantify uplift achieved from working the model, as well as tune and improve the model over time.
• Data Science lead responsible for feature engineering and CLV model development.
• Delivery Lead responsible for all aspects of project management and delivery.
• Reporting Lead responsible for the development and delivery of Excel based reporting.
• Author and deliver white papers, case studies, and presentations to senior business leaders.
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Technical Environment: AIX, SAS, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, Random Forest, Collaborative Filtering, K-Means, Oversampling, Stratified Random Sampling, Longitudinal Studies, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Out of Time Validation, Linear Programming & Convex Optimization.

CSAA developed a prescriptive model which would prescribe the next best action to take with a customer, and created a portfolio of… Show more

Technical Environment: AIX, SAS, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, Random Forest, Collaborative Filtering, K-Means, Oversampling, Stratified Random Sampling, Longitudinal Studies, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Out of Time Validation, Linear Programming & Convex Optimization.

CSAA developed a prescriptive model which would prescribe the next best action to take with a customer, and created a portfolio of associated reports, in support of increasing customer engagement, Customer NPS, Customer Retention, increasing revenue, and containing claims losses. Prescribed actions included specific upsell and/or cross sell actions, non-renewal action, giving a special offer or gift along with an appreciation letter, or taking no action. Deep dive adhoc analysis was used to answer business questions related to customer progression and journey and other customer and insurance portfolio insights. Control groups, A/B testing, and optimization was used to quantify uplift achieved from working the model, as well as tune and improve the model over time.
• Data Science lead responsible for feature engineering and CLV model development.
• Delivery Lead responsible for all aspects of project management and delivery.
• Reporting Lead responsible for the development and delivery of Excel based reporting.
• Author and deliver white papers, case studies, and presentations to senior business leaders
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Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, K-Means, Oversampling, Stratified Random Sampling, Longitudinal Studies, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Linear Programming & Convex Optimization.

CSAA modeled pricing elasticity and created a portfolio of associated reports in support of pricing decision science, forecasting, and portfolio insights. The model optimized revenue… Show more

Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, K-Means, Oversampling, Stratified Random Sampling, Longitudinal Studies, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Linear Programming & Convex Optimization.

CSAA modeled pricing elasticity and created a portfolio of associated reports in support of pricing decision science, forecasting, and portfolio insights. The model optimized revenue, pricing optimization, and pricing dislocations. Deep dive adhoc analysis was used to answer business questions related to price elasticity. Control groups, A/B testing, and optimization was used to quantify uplift achieved from working the model and tune the model over time.
• Supporting Data Scientist and report developer.
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Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, Random Forest, K-Means, Oversampling, Longitudinal Studies, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Linear Programming & Convex Optimization.

CSAA developed an algorithm to optimize underwriting exceptions and exclusions and created a portfolio of associated reports in support of sustainable growth and portfolio optimization. Deep dive adhoc… Show more

Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, Random Forest, K-Means, Oversampling, Longitudinal Studies, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Linear Programming & Convex Optimization.

CSAA developed an algorithm to optimize underwriting exceptions and exclusions and created a portfolio of associated reports in support of sustainable growth and portfolio optimization. Deep dive adhoc analysis was used to answer business questions related to underwriting exceptions and exclusions. Control groups, A/B testing, and optimization was used to quantify uplift achieved from working the model and tune the model over time.
• Supporting Data Scientist and report developer.
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Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, Random Forest, K-Means, Oversampling, Stratified Random Sampling, Longitudinal Studies, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Linear Programming & Convex Optimization.

CSAA modeled cost vs benefit of ordering underwriting reports and created a portfolio of associated reports in support of optimizing underwriting expense and return on… Show more

Technical Environment: AIX, SAS, SQL, Excel, MS Access, VBA, R, Python, PowerPoint, Generalized Linear Model, Random Forest, K-Means, Oversampling, Stratified Random Sampling, Longitudinal Studies, Imputation of Missing Data, Ensemble Learning, AdaBoost, Cross Validation, Linear Programming & Convex Optimization.

CSAA modeled cost vs benefit of ordering underwriting reports and created a portfolio of associated reports in support of optimizing underwriting expense and return on investment. The model would prescribe if certain reports should be ordered for certain individuals. Examples of underwriting reports which may be ordered included credit reports, motor vehicle reports, lexis nexus reports, car fax reports, etc. Deep dive adhoc analysis was used to answer business questions related to underwriting reporting ordering expense and return on investment. Control groups, A/B testing, and optimization was used to quantify uplift achieved from working the model and tune the model over time.
• Supporting Data Scientist and report developer.
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