SUMMARY:

• 10+ years of data science and data engineering experience in: Strategic Global data management, analytics, and interpretation
• Organizational and Managerial - analytics and business intelligence (BI) driven strategy development for IT and Business Management
• Client Relationship Management - analytics driven marketing, account management for tactical and strategic growth
• Data driven Business Process Reengineering - KPI implementation, benchmarking, quality process improvement
• Data migration and integration process plans, implementation, monitoring, and audit
• Cybersecurity, Technology and IT Audit - planning and audit scope development, internal control weakness gap assessment, risk exposure assessment in ERP systems, SaaS, and Cloud environments, risk-based internal audit, audits of financial, operational, IT, compliance/regulatory, or strategic business functions and related risks and controls, fraud, waste, and abuse and/or ethical/regulatory complaints related audits, detailed tests of controls including sampling/confidence levels, analytical procedures, Computer Assisted Audit Techniques (CAATs), IDEA, Audit Control Language (ACL), statistical and data analytics application to audit data, COBIT, NIST, ITIL, data driven cyber defense
• Bioinformatics and Clinical Data Science - Genome Wide Association Study (GWAS), translational bioinformatics, Cox model, logistical regression, random effect, mixed effected models and time-varying covariates, clustering, bioinformatics portals, graphs, graphical probability models, Next-Generation sequencing, exome sequencing, protein Prediction models, homology detection, Hidden Markov Models (HMM), Protein Data Bank (PDB), PFAM, sequence alignment, molecular dynamics, Chemoinformatics, Signal transduction modeling, gene expression analysis, ICD-9 and ICD-10, proteomics and metabolomics, Protein-DNA-RNA-Protein interactions, genetic analysis, linkage disequilibrium, ontologies, tools for drug discovery: QSAR, DOCK, Parallel phenotyping, precision medicine, data driven medicine, pharmacogenomics, OWL, Protégé, UMLS, EHR, XL-7, clinical text mining, Drug Safety (DS) Surveillance, classification algorithms, predictive models, drug discovery datamining, Drug-Drug interactions, clinical data de-identification, HIPAA compliance in applications, PharmGKB - pharmacogenomics centralized resource, expression quantitative trait loci (eQTLs), CRISPR Cas Genome Editing and Modeling
• Analytics driven contract management, supply chain optimization, logistics, demand planning, spend analytics, and best value sourcing
• Software development - group management, requirements, design, implementation, unit and integration test management, documentation, training, client acceptance test
• Development methodology - extreme programming, pair programming, test driven development, agile, waterfall, prototyping
• Database - SQL Server, Oracle, Postgres, DB2, MS Access, Apache HBase, MySQL. NoSQL, ETL, SQL, SparkSQL, TSQL, PL/SQL, PSQL, SOAP, XML
• Visualization tools - JSON/JavaScipt, Tableau, Qlik, D3, JS, Gephi
• Stack engineering & development - Elixir, Phoenix Framework, Elm, Ruby on Rails, CSS, GraphQL, Tailwind CSS, HTML, Nerves
• Data science environments - Tensor Flow, Azure. Google Analytics, SAP Hanna
• Statistical/Mathematical languages - R, Julia, Python, Pandas, SAS, MATLAB
• Programming languages, modules, and packages – Python, Pandas, ASP.NET, Classic ASP, JavaScript, Java, C, C++, C#, JSON, Scala, PySpark, Sparklyr, SparkR
• Large scale/ big data engineering tools – Hadoop Environment, HDFS, Apache Spark, Elastic Cloud Computing, Elastic Map Reduce(EMR), Parallel Computing, Multi-core code optimization, in-memory analytics, stochastic gradient decent
• Machine Learning (ML) – pipeline, baseline, ML workflow automation, supervised learning: SVM, Neural Networks, Multiple and Logistic Regression, Decision Trees, Random Forest, Bagging, Boosting, LDA, KNN, PCR, Cross Validation, Bootstrap, Dimensional Reduction, Regularization, Lasso, Ridge, Linear Model Selection, Unsupervised Learning: Various clustering techniques, PCA, ICA, reinforced learning, gradient decent, deep neural network
• Artificial Intelligence (AI) – AI algorithms, Reinforced Deep Learning, Cognitive Automation, Robotic Process Automation (RPA), Expert Systems, Fuzzy Logic, speech and voice recognition, image recognition
• Natural Language Processing (NLP) – machine translation, language detection, classification with different aspects of dealing with NLP like phonology, morphology, syntax recognition, bag of words, word based methods, semantics, pragmatics with different approaches of semantic analysis like distributional, frame based, interactive learning
• Fintech – multisource financial data scrubbing, sanitization, integration, and consolidation, analytics support for: Regtech - financial compliance, Insurtech – insurance process improvement, open banking support, robo-advisor support and quantitative algorithm development, optimization, and support
• Blockchain – application development for smart contracts, MRO (maintenance, repair and overhaul) in aviation industry, protection against GPS spoofing, Pharmaceutical data management for accuracy and relevance, visibility and transparency in drug supply chain
• Clinical image processing and semantic analysis – image modalities, visualization, filtering, registration, segmentation, computational feature extraction: geometric, texture, semantic, AIM, XQuery, SPARQL, radio-genomics, image segmentation and clustering
• Cloud computing – SaaS, IaaS, PaaS, virtualization, AWS(S3, EC2 - Elastic cloud computer, ELB - Elastic Load Balancing, simple storage service - S3, Elastic Block Storage – EBS, CloudFront, NoSQL, AWS databases - Relational Database Service (RDS) and Redshift, VPC (Virtual Private Cloud), EMR (Elastic MapReduce), Hadoop, SQS (simple queue service), SWS (Simple Workflow Service)), IBM Cloud, Microsoft Cloud, Oracle Cloud Service
• Bioengineering – biomechanics data modeling, data driven cardiovascular fluid mechanics, cell signal transduction modeling
• Synthetic biology and biochemical engineering – gene expression regulation, plasmid anatomy, genomic clone libraries, DNA fragment assembly, Next-Generation sequencing, DNA cell translation, homologous recombination, genome editing using CRISPR/Cas, engineering gene expression, DNA shuffling, codon usage, metabolic, protein- and bio-synthesis, analysis of engineered biological systems
• Established Data Scientist offering 10+ years of expertise in Data Science, Big Data Analytics & Strategy, Marketing Analytics, Cloud Computing, Cybersecurity and Information Systems Audit, Bioinformatics, Blockchain, Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), Software Development, Database Management, Visual Analytics, Stack Engineering and Development, Data Science Environments, Big Data Analytics, Fintech, Clinical Image Processing, Bioengineering, Synthetic Biology and Biochemical Engineering, Statistics, Data Mining, Management Science, MIS, etc.
• Has worked as a consultant and supported various end-clients across multiple verticals - Supply Chain, Investment Banking, Clinical, Aerospace, Cybersecurity, Ethics and Compliance, Biotechnology, Pharmaceutical, Finance, Banking, Biomedical, Investment Banking, etc.
• Was responsible for leading initiatives regarding development, marketing, and support activities for a supply chain management group decision support system for proposal evaluation, vendor selection, contract management, spend analytics, and related data analytics.
• Developed Big Data Predictive Analytics and AI Applications; developed deep reinforced learning and natural language processing applications for clinical and investment banking applications
• Candidate is from New York City and is available in the NY, NJ, and CT tri-state area. He is open to relocation and flexible to support onshore and offshore teams with different time zones, He’s actively seeking new opportunities and can start to work given 2 weeks’ notice to employer.

PROFESSIONAL EXPERIENCE:

Confidential

Strategic Lead Consultant

Responsibilities:

• Led global development efforts and marketed several data science, business analytics, and SaaS applications including cloud computing environments for marketing, CRM, Supply Chain, Investment Banking, Cyber Security, Clinical, Aerospace, Biotechnology, Pharmaceutical, Finance, Banking, Biomedical and Investment Banking.
• Used analytics and business intelligence (BI) for strategy development, IT compliance and governance, Fintech effectiveness, marketing, and supply chain management.
• Used big data engineering, clustering, supervised and unsupervised learning, and visual analytics to facilitate client relationship management, and targeted account management for sustained growth.
• Led the Development and Implementation of data driven business process reengineering, KPI implementation, benchmarking, and customer care quality process improvements for large eCommerce and eBusiness portals. Developed data strategy to define process solutions for migration of and integration of bulk and incremental data from various sources. Developed project plan contingencies and dependencies for data migration plans and processes. Developed mapping of data components between input and output systems in the integration and migration processes. Developed and implemented data test plans for migration and integration processes. Measured test results across various projects and integrate them into automated KPI monitoring mechanisms using descriptive and predictive analytics. Implemented contiguous audit on data integration and migration processes.
• Implemented internal cybersecurity, technology, and Information Systems (IS) audit for compliance, governance, and reliability of global SaaS, PaaS, and IaaS offerings. Developed and implemented data driven Computer Assisted Audit Techniques (CAATs), IDEA, Audit Control Language (ACL), statistical and data analytics application to audit information systems data. Used COBIT for governance and NIST cybersecurity framework for security compliance and benchmarks. For SaaS applications, implemented internal control weakness gap assessment, automated continuous audit, disaster recovery and business continuity plans, attainable RTO and RPO based on application, business needs and client sensitivity. Used big data analytics and large data set mining techniques to support risk exposure assessment in ERP systems, SaaS, and Cloud environments. Used data science and statistical techniques in risk-based internal audit, audits of financial, operational, IT, compliance/regulatory, or strategic business functions and related risks and controls, fraud, waste, and abuse and/or ethical/regulatory complaints related audits, detailed tests of controls including sampling/confidence levels, analytical procedures.
• Worked with large pharmaceutical and clinical data sets to implement bioinformatics, and translational bioinformatics based decision support systems. Used Genome Wide Association Study (GWAS) for disease prediction and personalized data driven medicine applications. Used Cox model, logistical regression, random effect, mixed effected models and time-varying covariates, and clustering in biomedical portal development and management. Used cyclic (directed and undirected) graphs and graphical probability models for clinical decision support portal development and management. Used large data set mining techniques to support Next-Generation sequencing, exome sequencing, protein Prediction models, homology detection, and Hidden Markov Models (HMM) for Protein Data Bank (PDB), PFAM, sequence alignment, and molecular dynamics related applications. Used big data engineering to support Chemoinformatics database alignment, signal transduction modeling and optimization, and gene expression analysis applications. Implemented HL-7 clinical data interoperability applications with XML/SOAP for ICD-9 and ICD-10. Used multicore parallel data engineering and distributed architecture for data mining of proteomics and metabolomics, Protein-DNA-RNA-Protein interactions, genetic analysis, and linkage disequilibrium. Developed clinical ontologies and data driven hierarchies. Used parallel processed big data analytics, OWL, Protégé, UMLS, EHR, HL-7, and clinical text mining to develop applications for drug discovery, QSAR, DOCK, Parallel phenotyping, precision medicine, data driven medicine, and pharmacogenomics. Used pipeline machine learning to develop SaaS for Drug Safety (DS) Surveillance, classification algorithms, predictive models, drug discovery datamining, Drug-Drug interactions. Automated clinical data de-identification for clinical trials and HIPAA compliant applications. Integrated PharmGKB– pharmacogenomics centralized resource with big data applications. Used machine learning and data mining techniques to identify and use expression quantitative trait loci (eQTLs), CRISPR CASP Editing, and biochemical modeling.
• Designed, developed, and marketed data driven supply chain SaaS environments for contract management systems, supply chain optimization, logistics, demand planning, spend analytics, and best value sourcing.
• Managed software engineering lifecycle for several data centric applications. Led and participated in group development efforts for requirements gathering, systems analysis, and design, coding implementation, unit and integration test management, documentation, training, and client acceptance test.
• Implemented extreme programming, pair programming, test driven development, agile, and prototyping development methodologies for data driven predictive SaaS applications. Used waterfall methodology for system integration and other applications.
• Used SQL Server, Oracle, Postgres, DB2, MS Access, Apache HBase, MySQL. NoSQL for building and deploying data warehouse and data driven applications. Used ETL, SQL, SparkSQL, TSQL, PL/SQL, Hive QL, PSQL, SOAP, and XML for data centric applications, predictive modeling with data mining and machine learning algorithms.
• Used visual analytics tools for data visualization and data centric story board generation. Used JSON/JavaScipt for dynamic visualization in SaaS deployments. Used Tableau, Qlik, D3, JS, Gephi for drilled down visual queries and visual representation of descriptive, prescriptive, and predictive analytics.
• In several SaaS projects used stack engineering & development techniques including Elixir, Phoenix Framework, Elm, Ruby on Rails, CSS, GraphQL, Tailwind CSS, HTML, Nerves. In addition used ASP classic, ASP.NET, JavaScript, JSON, CSS, and HTML for many SaaS applications.
• Used, migrated data into, and integrated with several data science environments such as Tensor Flow, Azure. Google Analytics, and SAP Hanna. Contributed with data driven business process knowledge to system implementation teams. In addition monitored and documented data migration process progress and related key performance indicators such as quality metrics of migrated data, critical process failures, and necessary corrective actions required.
• Performed data migration and integration of data from various sources as part of global data migration strategy.
• Proficient in statistical and mathematical languages and environments such as R, Julia, Python, Pandas, SAS, MATLAB for image processing, predictive modeling, data mining, Fintech, Clinical and bioinformatics, AI, and machine learning applications.
• Proficient in several programming languages, packages, modules and environments such as Python, Pandas, ASP.NET, Classic ASP, JavaScript, Java, C, C++, C#, JSON, Scala, PySpark, Sparklyr, SparkR. Used these environments for several data centric applications, SaaS development, and cloud computing.
• Selected the architecture, used and deployed large scale big data engineering tools and environments such as Hadoop, HDFS, Apache Spark, RDD API, elastic cloud computing, Elastic Map Reduce(EMR), parallel computing, multi-core code optimization, stochastic gradient decent, and in-memory Analytics for many applications in Fintech, Clinical and bioinformatics, supply chain, investment banking, aviation MRO, marketing, business strategy development, pharmaceutical and biotech.
• Developed automated pipeline machine learning process modules for preprocessing of large data sets from heterogeneous sources and networks. Developed flexible self-adjusting pipeline machine learning workflows. Developed techniques for post processing of large datasets for deployment of predictive models in clinical, pharmaceutical, financial, investment banking, and supply chain applications. Calibrated, normalized, transformed and flexible-fitted large datasets from heterogeneous data sources from global diverse networks.
• Used baseline machine learning techniques to predict performance of predictive models on future test data sets with high and low variance.
• Used several supervised learning techniques such as SVM, Neural Networks, Multiple and Logistic Regression, Decision Trees, Random Forest, Bagging, Boosting, LDA, KNN, PCR for predictive modeling in pre-emptive cyber defense, clinical, pharmaceutical, and supply chain applications.
• Used cross validation, bootstrap, dimensional reduction, regularization, Lasso, Ridge, linear model selection techniques to optimize predictive models in real world.
• Used several unsupervised learning techniques such as various clustering techniques (partitional, hierarchical, density-based, grid-based, spectral, stream, K-Maeans with MapReduce, visual, and interactive, semi-supervised, uncertain), principal component analysis (PCA), independent component analysis (ICA), and reinforced learning.
• Integrated deep reinforced learning techniques with AI cognitive automation techniques for clinical, investment banking, supply chain, and pharmaceutical applications.
• Implemented AI algorithms, reinforced deep learning techniques, cognitive automation, Robotic Process Automation (RPA), expert systems, fuzzy logic, speech and voice recognition, and image recognition techniques in many data centric and predictive modeling applications.
• Used NLP methods such as – machine translation, language detection, classification with different aspects of dealing with NLP like phonology, morphology, syntax recognition, bag of words, word based methods, semantics, pragmatics with different approaches of semantic analysis like distributional, frame based, interactive learning in pharmaceutical, biotechnology, clinical, and investment banking applications.
• Developed and implemented techniques for Fintech applications such as multisource financial data scrubbing, sanitization, integration, and consolidation. Supported and developed systems for Regtech - financial compliance, Insurtech – insurance process improvement, open banking support, robo-advisor support and quantitative algorithm development and optimization.
• Used Blockchain methodologies in application development for smart contracts in contract management SaaS, MRO (maintenance, repair and overhaul) in aviation industry cost reduction and productivity enhancement efforts, cyber protection against GPS spoofing for in-flight navigation systems and avionics, pharmaceutical data management for accuracy and relevance, visibility and transparency in drug supply chain.
• Designed and developed several applications for clinical image processing and semantic analysis as well as integration of structured and unstructured data. Developed video based eye tracking applications in Matlab, R, Python, and Pandas for neuropsychiatric assessment. Worked with various radiological image modalities. Used image processing techniques such as visualization, filtering, registration, segmentation, and computational feature extraction (geometric, texture, semantic, AIM, XQuery, SPARQL). Developed several radio-genomic applications. Used image segmentation and clustering algorithms for clinical applications and decision support systems.
• Used AWS - S3, EC2 - Elastic cloud computer, ELB - Elastic Load Balancing, simple storage service - S3, Elastic Block Storage – EBS, CloudFront, NoSQL, AWS databases - Relational Database Service (RDS) and Redshift, VPC (Virtual Private Cloud), EMR (Elastic MapReduce), Hadoop, SQS (simple queue service), SWS (Simple Workflow Service)
• Worked with IBM Cloud, Microsoft Cloud, and Oracle Cloud environments for big data applications. Developed architecture for SaaS, IaaS, PaaS, and virtualization for massive big data applications with heterogeneous global sources and different network architecture.
• Developed several biomechanics data models and integrated the same with predictive modeling and pipeline machine learning techniques to produce real world predictions for clinical diagnosis and prognosis.
• Developed several big data set driven cardiovascular fluid mechanics simulation and modeling techniques. Integrated radio-genomics into these models.
• Developed several big data set driven cell signal transduction modeling and simulation techniques.
• Used statistical data mining techniques in synthetic biology and biochemical engineering modeling such as gene expression regulation, plasmid anatomy, genomic clone libraries, DNA fragment assembly, Next-Generation sequencing, DNA cell translation, homologous recombination, genome editing using CRISPR/Cas, engineering gene expression, DNA shuffling, codon usage, metabolic, protein- and bio-synthesis, and analysis of engineered biological systems

Confidential

Technical Manager

Responsibilities:

• Managed a team of forty software engineers and design architects for developing statistical inference decision support systems.
• Worked with design Engineering lab to design and develop software related to microelectronics applications Member of the Technical Staff (MTS)
• Designed and developed microelectronic applications in C.