PROFESSIONALSUMMARY

Data Scientist & Data Engineer with 8 years of experience in the IT industry, specializing in Data Science, Data Engineering, Machine Learning, and Deep Learning.
Strong knowledge of Python, SQL, and data manipulation techniques for analysing complex datasets and deriving actionable insights.
Proficient in developing and deploying predictive models using machine learning algorithms such as Decision Trees, Random Forests, SVM, and Logistic Regression.
ExperiencedinNaturalLanguageProcessing(NLP),Chatbot,imageprocessing,anddeep learning frameworks - TensorFlow and Keras.
Skilledindatawarehousing,ETL(Extract,Transform,Load),andreportingtoolssuchas Informatica Power Center, SQL Server, Oracle, SSRS, and Power BI.
DomainexpertiseinHR/PeopleAnalytics,Pharma/LifeScience,Banking,andFoodIndustry.

SKILLS

Programming Languages: Python: NumPy, Pandas, scikit-learn, TensorFlow, Keras, Data structures, Linked list, stack, queues, trees, graphs, sorting and searching algorithms
DataAnalysisandManipulation:Data cleaning,ExploratoryDataAnalysis(EDA),Statistical analysis, Data visualization - Matplotlib, Seaborn
Machine Learning: Supervised learning algorithms (Linear Regression, Logistic Regression, Decision Trees, Random Forests), Unsupervised learning algorithms (Clustering, Dimensionality Reduction), NLP, Deep Learning (Neural Networks, CNN, RNN)
Data Warehousing and ETL: ETL processes, Data integration, Data modelling, Oracle, SQL Server, Informatica Power Center
DataVisualizationandReporting:SSRS,PowerBI.
Statistical Analysis and Modelling: Hypothesis testing, Regression analysis, Time series analysis
DomainKnowledge:HR/PeopleAnalytics,Pharma/LifeScience,Banking,FoodIndustry.

Data Scientist

Confidential Feb2022-Present Project-1: Risk and Control Assessment

Objective: Develop an NLP model to address discrepancies between ratings and reviewsgiven by managers to reporting employees.
Dataset:Consistedoftwocolumns(RatingandReview)withnonullvalues.
Featurization: Employed various techniques to extract relevant features, including wordmatch count and percentages of sentiment words. Used Hugging Face BERT model toidentify negation and finalize 19 features.
Model: Tested multiple algorithms like Logistic Regression, Random Forest, Naïve Bayes, with Random Forest achieving the highest accuracy of 0.81 after hyper parameter tuning.
Results: Model achieved an accuracy score of 0.81 on the test dataset, using Accuracy, F1, Precision, and Recall as evaluation metrics.

Project-2:YearlyAttritionPredictionModel

Objective: Identifyfactorscontributingtoattritionandunderstandwhyemployeesareleaving the organization. Develop strategies to address these factors and reduce attrition.
Dataset:IncludesCompensation,Ratings,Promotion,VOEsurvey,andGDPdata.
Featurization: Utilized various techniques for feature engineering, incorporating factors like recent promotions, rating changes, and appraisal differences.

Model: Explored multiple algorithms like Logistic Regression, Random Forest, XGBoost, Naive Bayes. After hyper parameter tuning, Random Forest achieved the highest recall of 0.78.
Results: Evaluated model using accuracy, F1 score, precision, and recall. Achieved thehighest recall of 0.78 on the test dataset, indicating effective identification of attrition factors.

SeniorData Engineer