Manav Parekh

Morris Plains, NJ ***** 908-***-****

https://www.linkedin.com/in/manav-parekh-55843590/ ad1noo@r.postjobfree.com SUMMARY:

● Pursued master’s degree Course in Applied Artificial Intelligence.

● Machine Learning/Deep Learning Projects: Advertising-Optimization, Customer Clustering for Target Marketing Campaign, Quitting Employee Identification, Whales Classification, Music Recommendation System, Question Answering Model using Google-BERT, and Face Detection and Identification.

● Grad-Course: Machine Learning, Deep Learning, Big Data, Data Structure and Algorithm, Applied Model and Optimization, Probability and Stochastic Processes, CUDA GPU Programming, C++, and Python

● Expert in Serialization Data Management with Systech-Software, ensuring DSCSA-FDA compliance. Proven track record in seamless data integration, hardware solutions, and cGMP adherence in pharmaceutical operations.

TECHNICAL SKILLS:

Artificial Intelligence:

Deep Learning, Regression Classification, Image Processing and Computer Vision, Natural Language Processing.

Framework/Application:

TensorFlow, PyTorch, Scikit-Learning, Django, OpenCV, Jupyter, Clion, PyCharm, Visual Studio, AWS-Sagemaker.

Programming

Languages/OS:

Python, C++, CUDA GPU Programming, Linux, and Windows. Serialization Engineering:

Product Master Data Configuration, XML file handling, GS1 Service, ZPL code for zebra printers, MySQL

EDUCATION QUALIFICATION:

Master of Engineering (ME) - Applied Artificial Intelligence January 2019 to May 2022 Stevens Institute of Technology Hoboken, NJ

Bachelor of Technology (BTech) - Mechatronics Engineering June 2013 to May 2017 Ganpat University Mehsana, Gujarat, India

WORK EXPERIENCE:

Leading Pharma LLC, Fairfield, NJ – Pharma/Serialization Engineer August 2017 to Current

• Proficient in managing Serialization Data Management Systems utilizing Systech-Software for meticulous product tracking and tracing in adherence to DSCSA-FDA guidelines.

● Demonstrated ability in crafting and maintaining seamless data integration connections with Contract Manufacturing Organizations (CMOs) and Customers, ensuring real-time updates on the status of pharmaceutical products at every juncture of the supply chain. Also handling Verification Routing Service

(VRS) request and response on errors query.

● Instrumental in the conception and implementation of robust hardware solutions at Distribution Centers, facilitating batch status updates, including unpacking, packing, and shipment status. My contributions play a pivotal role in ensuring compliance and operational efficiency within the pharmaceutical serialization landscape.

● Performing Qualification and Validation on semi and fully automatic machinery for the Drug Manufacturing and Packaging Department.

● Developing and Maintaining Facility to meet cGMP guidelines.

● Prepared Standard Maintenance System to standard maintenance program for all equipment and facilities. AI/ML PROJECTS:

● Quitting Employee Identification: Implemented different Regression Model like, Logistic Regression, Decision Tree, Random Forest and XGBoost to identify which employees are more likely to quit.

● Target Customers for Marketing: Grouping customers for the marketing team at the bank to launch a targeted ad marketing campaign using the elbow method to find the optimal number of clusters and implementing K-Mean Method.

● Whale Classification Kaggle competition, Identify Whales from their tails and classify them. Use boundary box Regression with Keras, TensorFlow.

● Music Recommendation System with Yahoo-Music Dataset, Kaggle competition created by Professor for Class, Archived first rank in class with 89% accuracy model. Implemented Alternating Least Squares Model with PySpark.

● NLP- Question Answering with TF2.0, Kaggle competition, Implemented BERT model on English Wikipedia Data to get short and long Answers for asked Question.

● Face detection and Recognition Using OpenCV, Face detection library, distributed computing on GPU and CPU to reduce learning time.

● Advertising-Optimization-Model using different Scikit-learn models and PuLP package to optimize investment and constraint budgets to maximize sales.

● Bowling Lane Booking Application for booking bowling Lanes. Applications also create bills based on time or number of games and number of players playing. Demonstrating Object Oriented Programming. https://github.com/mparekh3/Bowling_Lane_Booking

● Easy parsing using regex functions to find syntax errors in C++ code.

● Facial Application: Project covers tools for Face Recognition system including collecting new facial data, training model with new data, detecting face in real time and recognizing person. Project is on GitHub: https://github.com/mparekh3/Facial-Application

GRAD COURSES:

● Applied Machine Learning: Proficient in Linear Regression, Logistic Regression, Decision Tree, Ensemble Learning, SVM, and Feature Engineering.

● Big Data: Skilled in Time Series Analysis, Principal Component Analysis, Recommendation Systems, and Hadoop-to-Spark Programming.

● Deep Learning: Hands-on experience with Activation Functions, Backpropagation, Regularization, Deep Sequential Models (NLP: LSTM, RNN), and Image Classification (CNN, Resnet).

● Programming Languages: Advanced understanding of Python (Polymorphism, Immutability-mutability, Recursive Functions, OPP) and C++ (OOP, Multiple-Inheritance, Dynamic Memory, Mapping, Template, Multiple Threading).

● Data Structures & Algorithms: Proficient in Time Complexity Analysis, Sorting, Searching, Numerical Methods, Link List, Stack & Queue, Prime Numbers, and Number Theoretic.

● GPU & Multicore Programming: Expertise in Parallel hits, parallel stencil, reduction, scan, and Performance Analysis.

● Applied Modeling and Optimization: Strong background in Unconstrained, Constrained, and Convex Optimization, Numerical Algorithms, and Gradient Descent algorithms and analysis.

● Probability and Stochastic Processes: In-depth knowledge of Combinatorial Analysis, Conditional and Independence, Jointly Distributed Random Variables, and Properties of Expectation.

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