i have this goal named "Scale AI Impact"
and this is the work i have done
Thematic Topic Modeling:
Successfully performed a reverse knowledge transfer process and ran the thematic code end-to-end, leading to robust theme identification and insights generation.
Enhanced the output generated by Alteryx workflows by replicating it in Python, which streamlined our processes and improved workflow efficiency.
Data Preprocessing and Model Refinement:
Conducted extensive research on text classification algorithms and refined data preprocessing techniques, resulting in significantly cleaner datasets for model training.
Implemented key model adjustments, including parameter tweaking using Hyperopt, and ensured high data quality before model consumption.
Cause Model Development and Testing:
Incorporated new data into the existing cause model, addressing inconsistencies in output and optimizing the scoring methods.
Analyzed model outputs rigorously after testing on new datasets, refining processes based on insights gained, such as filtering stop words and handling abbreviations.
Enhanced Thematics Model:
Conducted robustness tests on the enhanced thematic model using train/test data, leading to refined topics and successful business reviews provided by Subject Matter Experts (SMEs).
Trained baseline models on various data categories (Cause, Consequence, Condition, Criteria), resulting in a substantial improvement in modeling outcomes.
Anti-Money Laundering (AML) Projects:
Played a pivotal role in AML projects by preprocessing and running thematic models on AML data, while sharing output in the required formats.
Developed keyword search processes to identify agent licensing issues and validate outputs with SMEs, ensuring accuracy and relevance of findings.
ChromaDB Integration and Validation:
Spearheaded the integration of AML and agent licensing data into ChromaDB, ensuring thorough preparation and validation of input data, which enhanced our database capabilities.
Performed metric calculations at the chunk level and validated outputs from the vector database, ensuring data integrity and usability.
Skills Demonstrated:
Analytical Expertise: Demonstrated strong analytical skills in various modeling techniques and data preprocessing.
Technical Proficiency: Proficiency in coding, particularly in Python, and familiarity with tools like Alteryx and ChromaDB.
Effective Collaboration: Collaboratively worked with SMEs to validate outputs and gain valuable insights into model performance.
Continuous Improvement Mindset: Actively sought ways to optimize workflows and improve existing models, resulting in higher efficiency and better results.
Conclusion
Through my contributions, I have not only advanced our data modeling capabilities but also ensured that our solutions are aligned with business needs and objectives. I am eager to continue building on this foundation, leveraging my skills and experiences to drive further success for our team. Given the scope of my contributions and their impact on our projects, I respectfully seek a salary hike that reflects my enhanced value to the organization.
 
FMLA Autoadjudication Process:
Model Development: Contributed to the creation and refinement of the FMLA autoadjudication model that checks for missing features and verifies whether required elements exist within the claims. This process assists Claim Specialists by providing preliminary checks and ensuring correct information flow.
Hit Rate Analysis: Engaged in analyzing and reporting the "hit rate" of the FMLA model, which assesses the accuracy and reliability of the model in identifying valid claims for automated processes. This allowed for better decision-making by the claims team.
Robotic Process Automation (RPA): Collaborated on implementing RPA solutions to automate manual claims processing. I ensured that the model correctly identified whether claims could be approved or denied based on the presence of necessary documentation and compliance with business requirements.
Monthly Reporting: Compiled and generated a comprehensive report to track claims processed through RPA each month. This report included:
Model approval rates.
Distribution of decisions (approved, denied).
Instances of system exceptions and claims not in good order.
Data Verification and Quality Checks: Conducted thorough checks on received RPA analysis files to ensure:
All required dates for the reporting month were present.
Accurate formatting and labeling of monthly report files for ease of access and retrieval.
Consistency in claim number generation by combining form numbers and DCN numbers.
Alignment of column orders to ensure accurate data processing and analysis.
File Management: Streamlined the process of downloading and organizing files from VDI, ensuring that the necessary data extracted from various tabs highlighted the key metrics required for reporting.
 

i want you to seggregate the work i have done with the goal name mentioned with reasoning
can u please give me the output based on this