SUMMARY

• Have more than 9 years extensive experience in Customer engagement and Data management and experience in analytics, digital or social media related with proven technical and analytical abilities. Strong Analytics and data management skills.
• Have 5+ years of data scientist experiences in various organizations and more than 5 years of data analysis and management experiences during my PhD work in physics, my experience includes strong problem solving, and advance statistical analysis skills and abilities.
• Furthermore, finished the computer simulation of filtering, guiding, trapping, and trajectory of cold molecular nitric oxide (NO) in the electrostatic, magnetic and electromagnetic field during my graduate work as PhD student.
• Have done a lot of prediction modelling work, social analytical work with our lab data that corresponds to the transition of elections from one energy state to other energy state when the molecules/atoms are exposed to laser light. I predicted the electron density, transition probability, and energy level in the different electronic states from the data collected using lab view in the lab and the data calculated from the theoretical model that I developed.
• Have practical understanding of statistical modelling and supervised/unsupervised/reinforcement machine learning techniques with keen interests in applying these techniques to predictive analytics world.
• Good for engineering, developing, deploying, and maintaining business systems with technical expertise including hands on solution development and implementation experience. I have ability to work effectively with diverse team of co - workers, and researchers, enthusiastic and self-motivated; flexible with adjusting to work schedules and team work needs; project management skills; research driven personality.
• Align closely with e-mail marketing and social analytics manager in order to define comprehensive measurement strategies for clients and to ensure delivery of actionable insights.Motivated and self-driven.
• Have ability to adapt to a fast pace and dynamic work environment with interpersonal, leadership and co-ordination skills.
• Exploring opportunities in data science, including deep machine learning, natural language processing, and artificial intelligence.

TECHNICAL SKILLS

Data Analysis: Generalized Linear Models, Logistic Regressions, Boxplots, K-Means, Clustering,SVN, PuTTY, WinSCP, Redmine (Bug Tracking, Documentation, Scrum), Neural networks, AI

SQL: MySQL, SQL Server, Oracle, SQLite, PostgreSQL

Programming Language: R - (Packages: Stats, Zoo, Matrix, data, table, OpenSSL). Java 1.7,1.8, maven, scala, spark 2,2.3, Spark Sql, Spark Streaming, Hadoop, mapreduce, HDFS

Python Versions: 2.7and 3.3, (Packages: NumPy, SciPy, Pandas, scikit-learn,Matplotlib, seaborn).

Microsoft Office: Excel, PowerPoint, Zeppline, Tableau Desktop (Version 10.0), MATLAB, Windows 8 and 7, LINUX Ubuntu and Mac.

PROFESSIONAL EXPERIENCE

Confidential

Sr. Data Scientist / Advanced Analytics Developer

Responsibilities:

• Captured and elaborated analytics solution requirements, working with customers and product managersCreated advanced analytics solution designs that the team of Advanced Analytics Developers and QA engineers can develop and test. Zeppelin, Spark SQL and MLLib was combined to simplify exploratory Data Science
• Gathered, documented, and implemented business requirements for analysis or as part of a long-term document/report generation. Analyzed large volumes of data and provide results to technical and managerial staffs. Recognize, report, and analyze trends that may develop from data stream.
• Apache Zeppelin multi-purposed web-based notebook was used to bring data ingestion, data exploration, visualization, sharing and collaboration features to Hadoop and Spark
• Took sole ownership of the analytics solution from requirements thru to delivery
• Worked with cross-functional teams, such as Product management, Platform, Operations and Mobile device engineering to elaborate on functional requirements/designs of the mobile wireless analytics modules, based on the product level requirements and designs
• Designed and develop advanced analytics software components and models architected for re-use
• Participated in all phases of the data analytics development process. Kafkawasusedasmessagebrokertocollectlargevolumeofdataandtoanalyzethecollecteddatainthedistributedsystem. Business data was feed into Kafka and then processing this data from Spark Streaming in Scala
• Collaborated with other software engineers, data scientists & QA to create solid technical designs, and robust, maintainable and testable implementations
• Zeppelin was used for data ingestion, data exploration, visualization, sharing and collaboration features to Hadoop and Spark.
• Spark Streaming was used to populatereal-time sentiments for crisis management and service adjusting
• Utilized statistical natural language processing to mine unstructured data, and create insights; analyze and model structured data using advanced statistical methods and implement algorithms and software needed to perform analyses
• Built document clustering, topic analysis, text classification, named entity recognition, sentiment analysis, and part-of-speech tagging methods for unstructured and semi-structured data
• Developed and perform text classification using methods such as logistic regression, decision trees, support vector machines and maximum entropy classifiers
• Developed methods to support and drive client and customer engagements focused on Big Data and Advanced Business Analytics, in diverse domains such as product development, marketing research, public policy, optimization, and risk management; Communicate results and educate others through reports and presentations
• Performed text mining, generate and test working hypotheses, prepare and analyze historical data and identify patterns
• Design, implementation and deep understanding of advanced statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques (e.g. regression,
• Provided subject matter expertise on models’ strength and weakness and recommend practical solutions. Discovered and diagnoses modeling related risks including input data, assumption, concept, methodology, process and implementation
• Discussed validation results with model owners and governance team to gain consensus and create strategies to implement changes if needed
• Utilized express mode validation for new model development, facing legal and compliance related modeling issues, quick model fixes, etc.
• Reviewed model documents, and conduct test runs on model codes. Provided different view on methodology and calculations, and provide feedback to model developers
• Comfort and proficiency in UNIX/HPC systems and experience using cloud compute (e.g. AWS, Azure). Familiarity with NoSQL databases, graphical analyses, and large-scale data processing frameworks (e.g. Apache Spark)
• Informed the selection of appropriate modeling techniques to ensure that predictive models are developed using rigorous statistical processes
• Established and maintain effective processes for validating and updating predictive models
• Analyzed, model, and forecast health service utilization patterns/ trends and create capability to model outcomes of what-if scenarios for novel health care delivery models
• Collaborated with internal business, analytics and data strategy partners to improve efficiency and increase applicability of predictive models into the core software products. Perform statistical analysis to prioritize to maximize success. Identify areas for improvement, communicating action plans
• Worked with both unstructured/structured data Machine Learning Algorithms such as Linear, Logistic, Decision Tress, Random Forests, Support Vector Machines, Neural Networks, KNN, etc)Linear, Logistic, Decision Tress, Random Forests, Support Vector Machines, Neural Networks, Clustering, KNN, graph/network analysis etc
• Performed strategic data analysis and research to support business needs. Identify opportunities to improve productivity via sophisticated statistical modeling
• Explored data to identify opportunities to improve business results. Develop understanding of business processes, goals and strategy to provide analysis and interpretation to management. Gain understanding of business needs and necessary analysis where appropriate through internal discussion

Confidential

Sr. Data Scientist

Responsibilities:

• Gathered, documented, and implemented business requirements for analysis or as part of a long-term document/report generation. Analyzed large volumes of data and provide results to technical and managerial staffs. Recognize, report, and analyze trends that may develop from data stream.
• Started with manually created Excel sheet and used Python programming language to create five summary tables, and create five tabs including formatting in Excel from Python programming language. Worked with tabular data that include filtered and scrubbed data, and tables with summed data for each collector per router and tables with 18 summary statistics including mean, standard deviation and moving average for number of ways. Wrote complex SQL programming language to interact with Aster database with more than 98 million rows using Teradata studio
• Worked with various data pools and DBAs to have access to data. Have knowledge of NLP, NLTK or Text Mining
• Developed new, innovative, data driven products in coordination with commercial, product management and technology platform teams to achieve competitive advantage. Provided consultative support to internal clients and/or teams working with clients. Advised internal clients on how their business problems can be solved with data science.
• Did statistical text mining to create a term-document matrix from a collection of documents. Trained and supervised learning up to 8 other team members for the SQL/R programming language and assist in the installation and upgrading of R-Studio Python and R. Advised and assisted others with their data mining, wrangling and formatting projects.
• Strong analytically skills to be able to interpret social media and engagement with the customers.
• Improve financial forecasting methodologies: Create ARIMA models for forecasting revenues along with unit root testing. Use K-means clustering for grouping similar data and documented.
• Extracted, transformed, and loaded data in Postgres data base using Python scripts.
• Have programming language such as Python (iPython), Java, SQL, Shell scripts, R, SAS, Machine learning tools: Apache Mahout, SAS, STATA
• Data visualization: Pentaho, Tableau, D3, Django web app. Have knowledge of Numerical optimization, Anomaly Detection and estimation, A/B testing, Statistics, and Maple. Have big data analysis technique using Big data related techniques i.e., Hadoop, MapReduce, NoSQL, Pig/Hive, Spark/Shark, MLlib and Scala, numpy, scipy, Pandas, scikit-learn.
• Worked to research and develop statistical learning models for data analysis. Collaborated with product management and engineering departments to understand company needs and devise possible solutions.
• SAS Data Analysts is used for analyzing client business needs, managing large data sets, storing and extracting information. Worked for feature engineering that involves converting the arbitrary data to well-behaved data such as dealing with categorical features, textfeatures, imagefeatures,and missing data.
• Worked with Core Java, and spring, Spring MVC and worked with Restful web services. Wrote and Tested Restful Webservices in SOAPUI
• Hadoop - MAPREDUCE/ Hive/ Pig/ was used to store, process and analyze huge amount of unstructured data having 100 million users to offer agiftcard to its top 10 customers who have spent the most in the previous year. Also found the buying trend of these customers so that company can suggest more items related to them. Kafka was used as message broker to collect large volume of data and to analyze the collected data in the distributed system.
• Splunk ES was used for application management, security, performance management, and analytics for the public APIs. Splunk was used for collecting, indexing, monitoring, and visualization of the data.
• Paxata was used to combine, clean and shape the data prior to analytics and it was also used to bring together data, find and fix dirty or missing data, and share and re-use data projects across teams. Designed and developed metrics, reports, analyses, and dashboards to drive key business and product decisions.
• Ability to understand business strategy, provide consultative business analysis, and leverage technical skills to create insightful, effective BI solutions
• Worked on data generation, machine learning for Anti-Fraud detection, Data Modeling, operations decisioning, and loss forecasting such as product-specific fraud, or buyer vs. seller fraud. Monte Carlo simulation algorithms were used to obtain numerical results by running simulations many times in succession in order to calculate probabilities with machine learning.
• K-fold cross Validation technique was used to improved model performance and to test the model on the sample data before finalizing the model. Confusion Matrix, Gain and Lift Charts, K-S Chart, and ROC Chart were used to evaluate the classification model.
• Worked with public/private Cloud Computing technologies (IaaS, PaaS & SaaS) and Amazon Web Services (AWS) and worked for customer analytics and predictions
• Kibana and Tableau was used for Business Intelligence tool for visually analyzing the data and to shows the trends, variations and density of the data in form of graphs and charts
• QlikView was used to create guided analytics applications as well as dashboards designed for business challenges. QlikView was used to uncover data insights and relationships across various sources, and improve the quality of business decisions
• Formulated procedures for integration of R programming plans with data sources and delivery systems and R language was used for prediction.
• Built advanced analytics solutions deployed into production for prediction, forecasting, and optimization, including: Data mining, Statistical analysis, Modeling, Machine learning, Visualization, Programming in R, SAS, Python and related tools, Big data tools for developing and executing models such as big data technologies like Hadoop, Hive, Pig and Spark
• Used query languages such as SQL, Hive, Pig and experience with NoSQL databases, such as MongoDB, Cassandra, HBase
• Applied statistics skills, such as distributions, statistical testing, both linear and logistic regression, SVM, decision tree, random forest, time series analysis, clustering, and neural network.
• Able to present reporting, insights and recommendations to clients clearly and succinctly.
• Worked with data visualization tools like Tableau and Kibana. Worked with SOA, IaaS, and Cloud Computing technologies, in the AWS environment. Experience with continuous software integration, test and deployment. Worked in agile software development paradigm (e.g., Scrum. Be up-to-date knowledge in the appropriate technical area
• Big data Analysis: Big data related techniques i.e., Hadoop, MapReduce, NoSQL, Pig/Hive, Spark/Shark, MLlib and Scala, numpy, scipy, Pandas, scikit-learn.
• Hands-on experience with rich UI and stunning data visualization using JavaScript
• Worked with both unstructured/structured data Machine Learning Algorithms such as Linear, Logistic, Decision Tress, Random Forests, Support Vector Machines, Neural Networks, KNN, etc)Linear, Logistic, Decision Tress, Random Forests, Support Vector Machines, Neural Networks, Clustering, KNN, graph/network analysis etc

Confidential

Sr. Data Scientist

Responsibilities:

• Worked on Deep Learning techniques including Neural Network, Logistic Regression, Decision Trees, KNN, SVM, Random Forest, Linear, Nonlinear, Boosted Regression Trees Keep up-to-date with latest technology trends.
• Organized large datasets so that we could get actionable insights from them that included finding innovative ways to combine fields of data that didn’t naturally mesh together. R language was used for evaluation and forecasting of data.
• Design, create, and execute digital analytics (web traffic) test scripts using software testing tools
• Develop all weekly, monthly and quarterly reports and dashboards
• Analyze site and media behavior in order to provide thoughtful, actionable insights and specific recommendations for optimization
• Design, create, and execute functional test scripts using software testing tools
• Built advanced analytics solutions deployed into production for prediction, forecasting, and optimization, including: Data mining, Statistical analysis, Modeling, Machine learning, Visualization, Programming in R, SAS, Python and related tools, Big data tools for developing and executing models such as big data technologies like Hadoop, Hive, Pig and Spark
• Serve as day-to-day analytics lead across all client engagement
• Used query languages such as SQL, Hive, Pig and experience with NoSQL databases, such as MongoDB, Cassandra, HBase
• Applied statistics skills, such as distributions, statistical testing, both linear and logistic regression, SVM, decision tree, random forest, time series analysis, clustering, and neural network.
• Design, create, and execute data quality test scripts using custom testing tools
• Worked with data visualization tools like Tableau. Worked with SOA, IaaS, and Cloud Computing technologies, in the AWS environment. Experience with continuous software integration, test and deployment. Worked in agile software development paradigm (e.g., Scrum. Be up-to-date knowledge in the appropriate technical area
• Worked with Big data related techniques i.e., Hadoop, MapReduce, NoSQL, Pig/Hive, Spark/Shark, MLlib and Scala, numpy, scipy, Pandas, scikit-learn.
• Collected data from all kinds of different sources, from web APIs, to internal databases encoded in SQL.
• Strong analytical skills both quantitative and qualitative
• Self-starter, with strong ability to initiate projects and see-through to completion
• Advanced PowerPoint, Excel and presentation skills.
• Used complex Excel formulas and pivot tables to manipulate large datasets.
• Used algorithms and programming to efficiently go through large datasets and apply treatments, filters and conditions to the data.
• Created meaningful data visualizations that communicated findings and related them back to business impact.
• Provided expert hands on execution of data analytics, machine learning, statistical modeling and general data science activities.
• Engaged significantly in hands on data hacking/munging, integration, cleaning, exploration, loading, gathering, etc. Advice and assist product management, software development and client implement teams to convert data science insight, data processing needs and results into meaningful business and technical requirement where relevant. Splunk ES was used for application management, security, performance management, and analytics for the public APIs. Splunk was used for collecting, indexing, monitoring, and visualization of the data.
• Supported testing of production code where relevant Participate in peer reviews of their work that that of other data scientists Support the management of the analytics processes, operations, procedures and data governance, security and protection. Leading, mentoring and developing Jr data scientists
• Coordinating with software development and product management teams at all stages of the service and software product lifecycle - acting as the data science focal point for all data science related activities whether they be internal or external clients.
• Ensuring data management complies with all local, regional and national legal and regulatory frameworks, paying attention to data controller. Be a local leader in a global team - work independently while communicating / coordinating effectively with the wider team and stakeholders.
• Used Eclipse RAD and RTC as an IDE. Used Tomcat, WebLogic, Web Sphere, and JBOSS as application server.
• Cross Validation technique was used to improved model performance and to test the model on the sample data before finalizing the model. Confusion Matrix, Gain and Lift Charts, K-S Chart, and ROC Chart were used to evaluate the classification model.
• QlikView was used to create guided analytics applications as well as dashboards designed for business challenges
• Converted and scrubbed data for uploading to MS SQL tables, using tools such as R, Python, Excel, and shell scripts (PDF to Text).
• Maintained data quality over data transitions and transformations, such as PDF to Text, to be read by R where data mapping and QC were maintained, to CSV, to Excel, to MS SQL Server.
• Created tables and reports from R and Excel, aggregating data across categories.
• Created queries to correct data issues in MS SQL tables. Built Excel and MS SQL tables to consolidate bank statements, including vendor data. Collaborated and assisted other business analysts with data issues and QC. Worked for feature engineering that involves converting the arbitrary data to well-behaved data such as dealing with categorical features, text features, image features, and missing data.
• Designed and tested a recommender system to provide alternative choices for employee donations to similar charities. Tableau and Kibana was used for data visualization such as trend lines, charts and graphs
• Time-Series Analysis and Predictive Modeling: using python and R. Statistical Analysis: Experience on Bayesian probability techniques, Hypothesis Testing, Hierarchical Modeling and Bayesian Inference.
• Built analytical solutions and models by manipulating large data sets and integrated diverse data sources. Performed analysis and develop reproducible analytical approaches to meet business requirements. Worked with data engineers on data quality assessment, data cleansing and data analytics.
• Performed exploratory and targeted data analyses using descriptive statistics and other methods. Applied machine learning and statistical techniques to large data sets to find actionable insights.
• Research new modeling techniques and evolving technologies. Presented results and recommendations to senior management and business users.
• Derived feature engineering and experimentation to improve model performance with a large set of proprietary data on user web behavior and social network
• Designed and architecture the data science product roadmap Led or actively participate in key initiative stand-ups, proactively advising on opportunities to apply and the best approach to apply the underlying algorithms, developed data design based on exploratory data analysis to meet stated business need.
• Collaborated with others in data science and analytics on data mining and predictive modeling as required, Created anomaly detection systems and constant tracking of its performance
• Selected features, building and optimizing classifiers using machine learning techniques. Extended company’s data with third party sources of information when needed
• Enhanced data collection procedures to include information that is relevant for building analytic systems Processing, cleansing, and verifying the integrity of data used for analysis
• Worked with team to select and implement model development process from statistics and/or machine learning to answer business problems.
• Reviewed and created repeatable solutions through written project documentation, process flowcharts and logs
• Worked closely with our engineering team to integrate and build algorithms. Processed unstructured data into a form suitable for analysis - and then to do the analysis.
• Extracted data from a variety of relational databases, manipulate, explore data using quantitative, statistical and visualization tools. Wrote complex SQL programming language to interact with Aster database with more than few million rows using Teradata studio
• Worked with both unstructured/structured data Machine Learning Algorithms such as Linear, Logistic, Decision Tress, Random Forests, Support Vector Machines, Neural Networks, KNN, etc)Linear, Logistic, Decision Tress, Random Forests, Support Vector Machines, Neural Networks, Clustering, KNN, graph/network analysis etc

Confidential

Sr Data Scientist/ Analytics Developer

Responsibilities:

• Gathered information from various programs, analyzed time requirements and prepared documentation to change existing programs.
• Built Social networks to reach audiences with effective advertising campaigns.
• Experience on K-means, Hierarchical Clustering, Mixture Modeling. Artificial Intelligence and NLP: Worked for feature engineering that involves converting the arbitrary data to well-behaved data such as dealing with categorical features, text features, image features, and missing data.
• Monitoring, analyzing, measuring and interpreting digital interactions and relationships of people, topics, ideas and content.
• Working knowledge on Natural Language Processing (NLP) and Natural Language Generation (NLG) using Python. Worked collaboratively with customers and team members supporting large business initiatives. Worked closely with other Data Scientists to build a better understanding on how Data Science integrates with the big database. Worked on the Data and Analytics Solutions team utilizing Scrum practices and techniques.
• JAVA/J2EE Application: implemented JAVA/J2EE technologies for application deployment using JSP, Servlets, Web Services (SOAP and Restful), Spring MVC, and Hibernate. Worked with business stakeholders identify business problems, opportunities and/or initiatives for which analytics models may provide insights that can contribute to or drive the development of an approach or solution.
• Conceptualized, designed and developed analytics models in addressing complex business problems, discovering insights and identifying opportunities that are of value to internal and external business stakeholders, typically using a hypothesis-driven approach.
• Worked with Data Engineers determine how to best source data, including identification of potential proxy data sources, and design business analytics solutions, considering current and future needs, infrastructure and security requirements, load frequencies, etc.
• Used Eclipse RAD and RTC as an IDE. Used Tomcat, WebLogic, Web Sphere, and JBOSS as application server. Worked with latest version of Spring as core business logic implementer - experienced in spring annotation/springboot/spring-data
• Data and analytics leaders from these agencies must take the lead to build new competencies in order to gain business advantage.
• Programmed SQL scripts inside R environment, including SQL commands such as joins over multiple tables.
• Scripted SQL ad hoc queries for testing database and explored data issues arising from other manual testing procedures.
• Created statistical column reports, using SQL queries to compare current inserted data against historical data by date, using commands sum-is-null, sum-zeros, minimum, maximum, average, and standard deviation. Rebuilt R scripts to more accurately reflect dividend yields. Built unit tests for script. Encapsulated data in R environment.
• Support the organization to derive value from data science and analytics. Extracted and analyzed data from internal databases as well as from external data system.
• Utilized data to support businesses model improvements and assisted businesses using data and analytics to help optimize profits
• Worked for all aspects of model building process: data aggregation, cleansing, building, validation, and implementation
• Effectively presented analytic results and communicate modeling computations to customers
• Coordinated communication with data vendors while tracking and documenting bugs and data issues through Redmine. Wrote macro view of markets and managed model portfolio, with emphasis on dividend-yielding stocks.
• Reviewed and independently tested the effectiveness and accuracy of Image Analytics, NLP and machine learning models Utilized expertise in models that leverage the newest data sources, technologies, and tools, such as machine learning, Python, Hadoop, Spark, Azure/AWS, as well as for Big Data.
• QlikView was used to create advanced reports from multiple data sources and Processed, clean, and verified the integrity of data used for analysis
• Enhanced data collection procedures to include information that is relevant for building analytic systems and experiences with cloud-computing.
• Worked with common data science toolkits, such as R, Weka, NumPy, skitlearn, MatLab, etc and experience with data visualization tools, such as D3.js, GGplot, etc.
• Used query languages such as SQL, Hive, Pig and experience with NoSQL databases, such as MongoDB, Cassandra, HBase and applied statistics skills, such as distributions, statistical testing, regression, etc.

Confidential

Graduate Teaching and Research Assistant

Responsibilities:

• Used theoretical knowledge and hands-on experience in statistical techniques to analyze the collected data; used Excel, Origin graph and computer programming to analyze data and to sketch and draw charts and other visual materials required to supplement explanatory text; communicated effectively with professors to support research work using PowerPoint.
• Took an effective scientific writing course for writing publications in scientific journals and or presentations; assisted in grant writing; attended scientific conferences and meetings and presented research posters/papers in many conferences and meetings; able to manage research budgets, resources and timelines.
• Calculated the trap depth along all directions of two, four, and six axially magnetized high-flux NdFeB permanent magnets that are positioned and aligned along three mutually orthogonal axes of rectangular bar magnets, ring cylindrical magnets and cylindrical magnets using computer programming.
• Experience in evaluating and monitoring social care tools and technologies, working directly with our vendors and key stakeholders across the organization.
• Calculated numerically the on axis and off axis magnetic field at any points between the two coils for both gradient coil and Maxwell coil using computer programming.
• Used computer programming to design and plan for magnetic trapping of neutral particles, and analyzed the result; executed gradient coil, and Maxwell coil using the big data.
• Used Mathematica to calculate the trajectory of the particles in the electromagnetic field, magnetic trap, magneto optical trap (MOT), electromagnet, gradient coils, stark hexapole guide, and permanent magnet Zeeman slower. Wrote LabVIEW programs and changed existing LabVIEW programs to collect data.
• Used computer programming to calculate the time of flight, nitric oxide rotational synthetic spectra, Zeeman splitting, convolution spectra, flux from the hexapole guide, and enhancement curves;
• Used SIMION software package to calculate electric fields and the trajectories of charged particles in those fields calculated spin density, relaxation times and flow and spectral shifts; experience with optimization of permanent magnetic trap, gradient coil, Zeeman slower, and stark hexapole guide.
• Designed magnetic trap using I-DEAS (Integrated Design and Engineering Analysis Software, a computer-aided design software package) and NX 7.5.

Confidential

Research Assistant

Responsibilities:

• Commercial pharma analytics experience in Life science Analytics domain.
• Researched and developed in Imaging technologies for Digital Pathology products and solutions.
• Utilized expertise in image processing and computer vision on initiatives pertaining to the development of imaging technologies for Digital Pathology.
• Investigated a wide variety of scientific principles and concepts resulting in potential inventions, products and problems.
• Served as an in-house and outside expert on imaging related applications.
• Researched, designed and developed new and robust solutions in image segmentation, registration, machine learning for analysis of digitized histopathology slides.
• Designed, conducted and led advanced independent or multi-disciplinary research driven by strategic business needs in digital pathology product development.
• Provided technical direction, mentorship, guidance and feedback to others.
• Strong experience in Customer Monitoring /Listening Platform and maintain the data base.
• R&D experience in one or more of the following topics: image processing, computer vision, machine learning and Medical Image Analysis.
• Hands on software development and implementation of image processing algorithms.
• Proven track record for innovations in problems solving and analysis.
• Good mathematical and analytical skills and through knowledge of basic and advanced image processing techniques.