Preethi B Email & Phone Number

As a Data Scientist at Wellcare, I spearheaded the development of AI-driven solutions to enhance operational efficiency and service delivery for healthcare providers. I implemented intelligent chatbots to automate routine inquiries, such as eligibility checks and claims adjustments, significantly reducing response times and improving provider satisfaction with instant, consistent answers. Additionally, I trained large language models (LLMs) on extensive datasets, ensuring they accurately handled complex queries on eligibility, claims, and authorizations.Key achievements included:-Developing a cloud-agnostic AI platform on GCP services, integrating machine learning algorithms like Random Forest, Decision Trees, K-NN, and deep learning architectures for classification, object detection, and segmentation.-Architecting scalable infrastructure using Vertex AI, BigQuery ML, and AI Platform Pipelines for model training, deployment, and inference.-Implementing robust ethical guardrails and continuous monitoring for LLMs to ensure regulatory compliance in healthcare.-Collaborating with cross-functional teams to design and deploy ETL pipelines with Apache Beam, Nifi, and GCP Dataflow, optimizing SQL queries and ensuring high data quality.-Creating deep learning models in PyTorch and TensorFlow for business-critical applications and designing object detection algorithms using OpenCV and Keras.-This work transformed healthcare operations by enabling data-driven insights and personalized recommendations for providers, improving decision-making efficiency, and ensuring ethical AI deployments.

College Station, Texas, United States

I have successfully created doctor performance report cards and financial/executive reports using Generative AI models and GCP's data analytics services to visualize key performance indicators over time. My expertise spans various machine learning techniques, including Decision Tree, Random Forest, Naïve Bayes, Logistic Regression, Cluster Analysis, and Neural Networks, tailored to specific business challenges within a Generative AI framework. I possess extensive experience with LLM technologies, particularly Transformer Encoder Networks, and have applied NLP techniques such as text mining, topic modeling, content classification, sentiment analysis, and entity recognition. My proficiency in data wrangling using NumPy and Pandas ensures high data quality from both structured and unstructured sources. Leading all phases of data mining, from collection to visualization, I've leveraged Tableau Desktop for interactive data visualizations and implemented multi-node clusters on GCP for forecasting temperature and humidity spikes in warehouses. Additionally, I developed real-time monitoring dashboards, explored text pre-processing techniques, and utilized NLP toolkits like NLTK, Genism, and SpaCy. My experience includes deploying IoT sensors for warehouse health monitoring, building machine learning models with Scikit-learn and GCP's ML services, and creating ETL pipelines using Hive and SQL for efficient data transformation. I have applied Generative AI techniques and supervised machine learning algorithms for predictive modeling, addressing diverse challenges in healthcare, customer segmentation, and forecasting. Through my work, I have integrated disparate data sources into a comprehensive data warehouse using GCP's data management solutions, ultimately enhancing data-driven decision-making and operational efficiency.

Little Rock, Arkansas, United States

In my role, I have effectively identified business problems and management objectives that can be addressed through data analysis, proposing innovative solutions and strategies to existing challenges. I have extensively analyzed, manipulated, and processed massive datasets using statistical software such as Jupyter, Sci-kit learn, and Tableau to uncover trends, patterns, and insights. By automating the entire data collection pipeline using ETL tools like Apache NiFi and Apache Beam, I streamlined processes and improved efficiency. I applied feature selection algorithms, including ANOVA and decision trees, using PySpark's MLlib package, and fine-tuned parameters to predict outcomes accurately. My expertise in designing and implementing predictive learning models and machine learning algorithms, such as K-NN and logistic regression, has been instrumental in financial applications like transaction classification and risk modeling, often combining them through ensemble learning. Utilizing data visualization techniques with tools like Tableau and libraries such as matplotlib, ggplot2, and seaborn, I created compelling visual representations of data analysis results. I continuously stayed abreast of emerging analytic trends and technologies by reading research and scientific articles to enhance the efficiency of existing models. My role also involved performing quality analysis testing and validation using Django, reformulating models for accurate predictions, and conducting end-to-end API testing with both dummy and actual data before product launch. I have presented findings internally for peer reviews, to clients, and collaborated closely with several teams including engineering for product deployment, product teams for launch, and reporting teams for the final design template. Additionally, I prepared documentation and delivered presentations of mathematical modeling and data analysis results to stakeholders.

Detroit, Michigan, United States

As a Data Analyst at BCBS, I conducted qualitative and quantitative research to extract insights from our data mart. My role encompassed data identification, collection, exploration, and cleaning for modeling purposes, actively participating in model development. Utilizing statistical models and machine learning techniques, I derived actionable insights from transaction data, contributing to strategic decision-making processes.Collaborating with stakeholders, I gathered requirements and defined multiple dimensions, extracting and organizing data from diverse sources including source files and databases. Managing a comprehensive sales database, I ensured data integrity through meticulous cleaning, filtering, and transformation processes. Employing various reporting tools, I designed intelligent reports to effectively communicate findings to key stakeholders.Proficient in R and Python, I utilized ggplot2 and matplotlib for advanced data visualization, creating insightful visual representations of our data. My expertise extended to operating within the Amazon Web Services cloud computing environment. Additionally, I conducted exploratory data analysis and statistical modeling to identify product performance trends, employing techniques such as classification, treemap analysis, and regression modeling. Regularly, I developed dynamic dashboards and visualizations using tools like Tableau Tabpy and ggplot2, facilitating informed decision-making processes.Throughout my tenure, I demonstrated proficiency in business intelligence and data visualization, applying concepts of probability, distribution, and statistical inference to uncover valuable insights within our datasets. My contributions ranged from data collection and organization to interpretation and presentation of statistical information, driving actionable outcomes for BCBS.