SUMMARY:

• 7+ years’ experience as Assistant Professor / Research Assistant in Mathematics.
• Completed Data Analytics, Big Data and Predictive Analytics Certification from Ryerson University.
• A+ grade/highest GPA in Data Analytics certification program supports my potential to execute project work in any organization.
• Strong Analytical and Mathematical Skills in both pure and applied Mathematics.
• Expertise in Statistics, Calculus, vector analysis, Linear Algebra and Linear programming, Boolean Algebra etc. which are essential hooks to understand data analysis and mining techniques.
• Excellent understanding of Predictive Analysis and Machine Learning techniques.
• Advanced concepts including classification and clustering algorithms like Neural Networks, Gradient boost, Logistic and Linear regression, k - NN, Random Forests, Naïve Bayes, Decision tree and kmeans etc.
• Advanced knowledge of Data Visualization, strong conceptual knowledge of Explanatory and Exploratory Analysis.
• Good understanding of Natural Language Processing and Text Analysis
• Performed data quality assessment and prepared the data set for analysis.
• Performed Explanatory and Exploratory analysis on the dataset.
• Applied the feature selection methods and ranking the features. Selected the relevant features out of 105 variables.
• Compared the results of KNN, Random Forests, Support Vector Machine, Gradient Boosting, Logistic
• Regression and Neural Network models, trained and tuned using cross validation and smote techniques on the training dataset.
• Discussed the variance of the models and performing the hypothesis testing to test the difference between the models.
• Built the final model by ensembling the models.
• Presented the results to a team of instructors and fellow students.
• Collaborated with a team of 5 data scientists to build a model that determine the customers who are likely to subscribe to a long - term deposit account, and to devise an effective telemarketing strategy to reach these customers. (Tools Used R and Weka)
• Performed data quality assessment and prepared the analytical data science set for modeling. Used R for outlier / extreme values detection, descriptive analysis for numerical / categorical attributes and features selection.
• Picked the key attributes based on correlations, near zero variance and low information.
• Compared the Decision tree, Na ve Bayes, Random forests and used n-Fold methodologies on training sample
• Built the final model on training sample and checked its performance on test sample
• Provided the recommendation by using K-mean clustering and association rules based on the final model (data mining).
• Presented the results to a team of instructors and fellow students.

TECHNICAL SKILLS:

Languages: R, Python, HTML, XML

Machine Learning Software: Weka, IBM Watson, Tableau

Big Data Analytical Tools:

Hadoop platform: HDP,MapReduce, HDFS, Hive, Pig, Hbase, Spark, Ambari etc.

Database Systems: Advanced SQL techniques, SAS, MongoDB, MS -Excel.

Operating System: Windows, Linux Ubuntu, Unix ggplot2, caret, Numpy, matplotlib, Pandas, Regular Expressions, R Markdown, Jupyter Notebook, Visual Studio Code and many more.

PROFESSIONAL SOFT SKILLS:

• Effective communication skills (both written and verbal)
• High level organization skills and the ability to multi - task
• Excellent time management skills
• A very collaborative and a strong team player
• Strong focus on results-oriented approach those can generate a business value.
• Excellent Presentation skills -can present the findings and results of the project to the wide audience.

WORK EXPERIENCE:

Assistant Professor (Mathematics)

Confidential

Responsibilities:

• Prepare and deliver the lectures on various topics of Mathematics (Pure and Applied) for E2E requirements of
• Undergraduate courses. Taking tutorials and problem-solving sessions for the courses.
• Managing the assessment of students through various procedures.
• Preparing the final exam papers and checking the answer scripts for the university.
• Taking part in various duties when called by university for Examination, Admission, Course Designing and Module preparing etc.
• Part of Management teams for admissions, examinations, cultural and Annual Function, Library, Magazine and various others.
• Taking Part and attending the workshops, seminars, trainings and guest lectures etc. for teachers and students in the college and University.
• Supervision and demonstration of the lab work.

Lecturer / Research Assistant

Confidential

Responsibilities:

• Lead the workshops and group discussion sessions of students and other faculty members.
• Preparing the modules of various areas of Mathematics for Middle, High and undergraduate students.
• Delivered lectures during the workshops for students at various levels.
• Taking part and attending research seminars and guest lectures.
• Demonstrate interdisciplinary use of mathematics through engagements with various departments like
• Statistics, Economics, Physics and Commerce etc.
• Lead social drive to host dedicated Telephonic Help Line and TV program to assist High School Student for
• Stress management and preparing them for real exam scenarios.

Tools: used - Excel, Latex, Scientific work place, Fortran