STAND 8 is a global leader providing end-to-end IT Solutions. We solve business problems through PEOPLE, PROCESS, and TECHNOLOGY and are looking for individuals to help us scale software projects designed to change the world!
We are looking for a Data Science, This could be a great opportunity for you!
Tools
AA Techniques or Statistics
Tools
- o Python: Proficient
- o SQL: Proficient
- o Process Orchestration, Data Pipelines, using Airflow: Good
- o Spark: Very Good
- o Bitbucket: Very Good
- o Unit Testing, Error Logs, Resilience, Code Modularity: Good
- o MLOps: Good
- o AWS: Good
AA Techniques or Statistics
- Statistics: Proficient
- Machine Learning (Including DL - Not Convolutional Network Knowledge for Images is Necessary): Proficient
- NLP - Good
- Loss generation savings in Independent Agents (MA and CT): Score the agents based on multiple variables to predict their losses/savings and prevent losses in advance - ML Model, Unsupervised Learning.
- MVR Models Retraining: Predict if it is necessary to order or not, a MVR - GLM or decision tree (likely), supervised model.
- ECC Proactive outreach model: Achieve call deflection and call first resolution after applying NLP in call (text) data. NLP: Text and Sentiment Analysis.
- Next Claim Prediction: For those policies that have a claim, predict the likelihood of having a second or a third one. ML model, supervised.
- MA HO Dataset Building - Building a common dataset that can be used to create the MA HO loss cost models, retention, …
- MA Auto Commercial Lines Retention Model.
- POC: Conversion model using Boston Software Data: Train a conversion model including our competitors pricing captured in the comparator by Boston Software. Hybrid: GLM and ML model, supervised learning.
- MA Auto UW models: Filter “bad” risks - Reuse the loss cost models for this.
- AA Pipeline: Creating modular python code that will be used for the whole AA team. This will allow them to do EDA, Feature Engineering, Modeling and Validation in a nearly automated way.
- WA ACMIC HO PLE: Predicting PLE for WA HO - Lower priority (2024)
- Advanced English
