Jadav Saikrishna

– Designed, developed and maintained daily and monthly summary, trending and benchmark reports in Tableau Desktop. – Automated tableau reports to create dashboards on daily incoming data. – Analyze the chatbot performance and monitor the onclick data through chatbot and present the results. – Performed text analytics like tokenization, stemming, converting words to vectors using NLTK, Gensim, Spacy. – Used Pandas, NumPy, seaborn, SciPy, Matplotlib, Seaborn, Scikit-learn, NLTK in Python at various stages for developing machine learning model and utilized machine learning algorithms such as linear regression, naive Bayes, Random Forests, Decision Trees, K-means, & KNN. – Used Principal Component Analysis and Truncated svd to analyze high dimensional data – Used correlation analysis in data exploration stage and graphical techniques to get some insights about the data. – Created various types of data visualizations using Matplotlib, Seaborn and Tableau. – Implemented Key performance Indicators (KPI) Objects, Actions, Hierarchies and Attribute Relationships and better performance of SSAS warehouse – Created and designed reports that will use gathered metrics to infer and draw logical conclusions of past and future behavior. – Build power BI reports to show various KPI's about the business and complete order life cycle from order purchase to order shipments – worked on Complex Scoreboard, Heat maps, tree views, Circle views, Histogram visualizations and interactive dashboards to find the trend analysis of data – K Means clustering to identify and classify unlabeled data. – Environment: Cluster Analysis, Regression, Natural Language Processing, GCP, Microsoft Azure, Sql Server, Logistic regression, SoftMax classifier, Random Forest, Python, R, PySpark, SQL, Oracle 12c, NLTK, Recurrent Neural Networks, LSTM cells, Natural Language Toolkit, NumPy, SciPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, Tensor Flow, Keras.

– Performed Exploratory Data Analysis using Pandas and Matplotlib to uncover insights and identify anomalies. – Applied Principal Component Analysis for high-dimensional data in feature engineering, eliminating irrelevant features. – Utilized a diverse range of machine learning methods, including Classifications, Regressions, Dimensionality Reduction, and Clustering techniques. – Achieved customer segmentation through clustering algorithms, leading to improved target marketing and a 10% reduction in marketing budget. – Designed and developed Recommendation models using Content-based and Collaborative filtering. – Evaluated model performance using techniques like cross-validation and log loss function. – Conducted A/B testing to monitor and validate new initiatives. – Developed NLP models for Sentiment Analysis of customer reviews. – Addressed overfitting and underfitting through hyperparameter tuning and L1/ L2 Regularization. – Created various data visualizations using Matplotlib and Tableau to communicate findings to data and marketing teams. – Created data analysis pipelines in Python using Pandas, NumPy, and Matplotlib for streamlined exploration and visualization. – Automated data loading from diverse sources (transactional databases, APIs, flat files) into Snowflake, Redshift, or BigQuery using Python scripts and cloud tools. – Led integration and harmonization of data from various EHR systems using health record system data dictionaries. – Ensured healthcare data compliance with HIPAA, HITECH, and GDPR through access controls, encryption, and auditing in SQL databases. – Loaded processed health claims data into Data Warehouses (e.g., Snowflake, Redshift), optimizing for storage and query performance. – Coordinated with project personnel in JAD sessions using Agile, iterative techniques to gather requirements.

– Performed Time series analysis on data. – Analysed large datasets to develop multiple custom models and algorithms for innovative business solutions. – Performed data profiling, preliminary data analysis, and handled anomalies (missing, duplicates, outliers, irrelevant data). – Involved in Analysis, Design, and Implementation of Business User requirements. – Experienced in using supervised, unsupervised, and regression techniques in building models. – Performed Market Basket Analysis to identify asset groups moving together and recommend client risks. – Determined trends and significant data relationships using advanced Statistical Methods. – Implemented forward selection, backward elimination, and stepwise approach for significant independent variable selection. – Performed Feature selection and Feature extraction dimensionality reduction methods to identify significant variables. – Used RMSE score, Confusion matrix, ROC, Cross validation, and A/B testing to evaluate model performance. – Performed Exploratory Data Analysis using R, and generated graphs and charts using Python Libraries. – Led a data migration project in the British Petroleum (BP) logistics domain, ensuring seamless transition of critical operational and financial data to a new enterprise system. – Designed and implemented data transformation workflows for logistics and supply chain data processing. – Developed SQL queries for data validation, improving reporting accuracy and operational efficiency. – Conducted data integrity audits, reducing inconsistencies by 30% in supply chain and transportation records. – Created Business Requirements Documents (BRDs) and documented functional and non-functional requirements for the data migration project. – Developed and executed test plans, including User Acceptance Testing (UAT), to validate accuracy and completeness of migrated data.