SUMMARY:

• An adroit Data Scientist with deep knowledge of statistical theory and state - of-the-art data mining techniques. Hands-on experience with machine learning algorithms in Python. Looking for Data Analysts and Data Scientist positions
• Project Manager and Development Engineer with over 15 years of experience working in R&D and Engineering environments.
• Extensive Experience in Data Analytics, Data Science, Possess Project Planning experience, including Strategic Planning, Budget and Schedule Estimation, for both Qualitative and Quantitative Analysis.
• Detail-oriented and driven engineer with knowledge of Statistical and Mathematical Data analysis for continuous improvement, failure analysis, cost analysis, work standards and measurements, statistical analysis. Proven ability to evaluate machine learning algorithms/practices for Regression and Classification categories to recommend steps for continuous improvement that drive efficiency, productivity, and cost savings.
• Capable of conducting Data analysis, Data Wrangling for Machine Learning Algorithms.
• Familiar with Statistical Process Control, 6igma, Statistical Methods, Standard Logistics, Simulations.
• Thrives in fast-paced environments where efficiency is key and excels at working both independently and as part of a high-performing team.
• 15 years of experience in R&D and Engineering Environments that includes Design, analysis, and development of various data predictive analysis for industrial applications.
• Expertise in data acquisition, analysis, data wrangling, visualization, data interpretation.
• Hands-on-experience on various python libraries such as Numpy and Pandas for large data sets manipulation, data cleaning, quality control, and data management
• Hands-on-experience with visualization libraries in python such as Matplotlib and Seaborn for designing graphs as per the business needs
• Experience with statistical analysis on data, predictive modelling with accuracy and error parameters.
• Expertise in design and develop analytics, machine learning models for Regression and Classification Problems.
• Hands-on experience using Linear, Linear Multi-Variant, Polynomial, Decision Tree, Random Forest, Support Vector Methods for Regression based Machine Learning Algorithms
• Experience of implementing Logistics, Kernel based SVM, Naïve Bayes classification, Random Forest based Classifications.
• Familiarity with k-Fold Cross Validation for Grid search and XG Boost (for Linear & logistic Regression, KNN), and visualizations using Python
• Expertise in data analysis, visualization (ggplots2), linear, logarithmic regressions using R
• Data analysis and mathematical modelling using Matlab
• Knowledge of using Anaconda, Pychart, Jupyter, Spider, GitHub etc.
• Extensive finite elemental modelling using Confidential and ANSYS for predictive simulations in micro-electro-mechanical systems.
• Extensive experience in Design of Experiments using Factorial methods, 6sigma, Cp, Cpk, control charts, yield analysis for production control, statics analysis
• Data manipulation using SQL and good understanding ETL (Extraction, Transformation, Loading)
• Data Reporting using Microsoft suites (Power BI, Power Point, Access, Excel, Project)
• Experienced to calculate metrics, condensed multiple data sources and large amounts of data into concise targeted information summaries and reports for management.
• Experience in product yield analysis using various graphical representations such as Pareto diagrams, histograms, scatter plots.
• Knowledgeable that drive performance and provide insights, from prototyping to production deployment
• Experience in Leading Teams for multiple projects with good communication and mentoring skills
• Experience in Project Management from Semiconductor Industry

KEYWORDS:

Data Analysts, Data Science, Python, Machine Learning, Numpy, Pandas, Matplotlib, Seaborn, SK Learn, Linear Regression, Polynomial Regression, Multivariate Linear Regression, K-Nearest Neighbors, Supporting Vector Machine (SVM) Learning Algorithms, Decision Tree and Random Forest Algorithms Kernel based SVM, Naïve Bayes classification, k-Fold Cross Validation, and XG Boost for Regression and Classification. Working knowledge of R, MATLAB, Mini-Tab, Micro-Cal Origin, Microsoft suite, Power BI etc.

PROFESSIONAL EXPERIENCE:

Development Engineer

Confidential, Garland, TX

Responsibilities:

• Led development activities for new concepts of infrared detectors. This includes development of detectors as well as parametric performance analysis.
• Analyse performance metrics to identify yield limiting processes or components, applying sound statistical analysis and effective root cause analysis to identify process improvement opportunities.
• Led design of experiments to determine the processes and variables that have the greatest effect on products and determine which sets of variables result in a more robust and high performing product (using Linear and Multivariate Linear Regression Models)
• Electrical and electromechanical data analysis using various mathematical tools such as Python, Microcal Orign, MinTab, Matlab, Excel etc.
• Generated product yield analysis using various graphical representations such as Pareto diagrams, histograms, scatter plots.
• Data clinging, data wrangling for predicative modelling using Linear, multivariate models and statistical models based on Log-normal distributions, Weibull analysis
• Familiar with 6igma, 2k factorial methods for design of experiments and associated statistical analyse
• Predicted successfully performance of detectors based on device specifications using Multivariate Linear Modelling
• Project management with scheduling, tasking, risk analysis, project integration skills

Engineer

Confidential, Ann Arbor, MI

Responsibilities:

• Analyse product performance at all stages of development, manufacturing, and customer integration to report yield metrics of an advanced micro-bolometer infrared detector manufacturing business.
• Utilized data collection, database analysis, data visualization, and failure analysis techniques to establish and track yield improvement projects, focusing on root cause problem analysis.
• Completed statistical data analysis (wafer to wafer, lot to lot, product to product, experiments, etc.) and made recommendations for product and process improvements with performance prediction.
• Designed and developed analytics, machine learning models (Linear, Multi-Variant regressions), and visualizations that drive performance and provide insights, from prototyping to production deployment
• Created Database, Tables, Visualization, bar charts for real time data analysis
• Generated and presented multiple data analyses in Excel, Power Point formats and published using Power BI dashboards to internal as well as external stake holders.
• Interface between external industrial contractors and internal engineering groups

Senior Development Engineer

Confidential, Pittsburgh, PA

Responsibilities:

• Lead projects through conceptual component design and development for Confidential detectors. Developed prototype infrared detectors and evaluated metrics for electrical, parametric, reliability in detectors.
• Analysing system requirements, decomposing requirements to software subsystems, and defining testing criteria of the requirements at device, software and system level
• Generated and presented multiple data analyses in Excel, Power Point formats and published using Power BI dashboards to internal as well as external customers
• Data Analytics Engineer for Engineering Metrics & Analytics team.
• Operationalize, publish, and monitor successful models to shape business and data science strategy
• Translate complicated statistical modelling concepts in an easy-to-understand manner to effectively communicate the value and benefits of the statistical findings to business users and senior executives

Researcher

Confidential, University Park, PA

Responsibilities:

• Developed Micro-Electro-Mechanical Devices using controlled experiments based on computational models using Matlab
• Developed Confidential actuation and micro-sensor predictive analysis using Finite Elemental Simulations using Confidential to predictive device performance and co-related with experimental results
• Developed nano-second laser annealing modelling using a time series based Confidential finite elemental analysis.

Post-Doctorate

Confidential, University Park, PA

Responsibilities:

• Perform Bench-level characterization and validation of Confidential devices using fabrication process of microelectronics
• Conduct analyses, characterization, and validation of new product architecture/platforms