Advise SME leaders on generative AI capabilities for automation opportunity and productivity improvement. Collaborate with business stakeholders and technical specialists to engineer generative AI solutions on cross-functional use cases. About one-half hands-on in leading generative AI engineering projects from end-to-end.See more under Accomplishments - Projects.

Overview Advised cross-functional corporate and product business teams on cloud-based machine learning (ML) solutions for business growth and transformation strategies. Collaborated with business stakeholders and technical specialists to engineer ML solutions on customer profiling, market-basket analysis and customer loyalty problems. About one-half hands-on in leading the ML engineering projects from end-to-end. Mentored 1-2 junior data scientists.Responsibilities* Serve as the ML technical lead on large and complex product development projects for the cloud, from ideation and scoping to implementation and deployment, under agile approach.* Collaborate as ML engineer in product development teams to build cloud solutions for complex business problems. * Liaise with cross-functional business stakeholders to define and disambiguate business objectives, set project scope, assign business value drivers and related KPIs.* Spearhead robust solution architecture to assign suitable methods (algorithms) and to create technical (sprint) plan to coordinate data engineering and ML tasks.* Lead the machine learning process—from data exploration and preparation to feature engineering, statistical modeling, validation and interpretation, and pipelines for deployment.* Execute production code quality review, model parameter calibration, pipeline optimization for production deployment. * Monitor production performance for models' scalability, efficiency loss, drift.* Showcase solutions through visualization prototypes and "storytelling" with emphases on communicating actionable data insights, implications for KPIs, recommending new use case opportunities. * Mentor junior data scientists and identify and prioritize their development needs.* Keep up-to-date on advancements in ML/AI technology through continuous learning and for purpose of informing ML/AI vision, strategy and investment priorities.See more under Accomplishments - Projects.

Engineered detection capability to proactively identify freight shipment problems.See more under Accomplishments - Projects.

Advised cross-functional corporate business teams on on-premise and cloud-based machine learning (ML) solutions for transformation strategy. Collaborated with business stakeholders and technical specialists to engineer ML solutions on employee compliance, software product development and valuation, client contract bidding, airfare travel cost and marketable security liquidity problems. About two-thirds hands-on in leading the ML engineering projects from end-to-end. Mentored 1-4 junior data scientists.Responsibilities* Serve as the ML technical lead on large and complex product development projects for the cloud, from ideation and scoping to implementation and deployment, under agile approach.* Collaborate as ML engineer in product development teams to build cloud solutions for complex business problems. * Liaise with cross-functional business stakeholders to define and disambiguate business objectives, set project scope, assign business value drivers and related KPIs.* Spearhead robust solution architecture to assign methods (algorithms) and to create technical (sprint) plan to coordinate engineering tasks.* Lead the machine learning process—from data exploration and preparation to feature engineering, statistical modeling, validation and interpretation, pipelines for deployment.* Execute production code quality review, model parameter calibration, pipeline optimization for production deployment. * Monitor production performance for models' scalability, efficiency loss, drift.* Showcase solutions through visualization prototypes and "storytelling" with emphases on communicating actionable data insights, implications for KPIs, recommending new use case opportunities. * Mentor junior data scientists and identify and prioritize their development needs.* Keep up-to-date on advancements in ML/AI technology through continuous learning and for purpose of informing ML/AI vision, strategy and investment priorities.See more under Accomplishments - Projects.

Held IT Management faculty positions at universities with accredited business degree programs. Social science researcher with research methods grounded in quantitative analyses and statistical models that are widely employed in other disciplines including finance, marketing, economics and much medical research. Research methods include field and lab experiments. Select research projects are described below and a complete publication list is available on request.Teaching experience includes data science, statistics, data management & database design, programming, strategic perspectives of IT management, IT and innovation, and business models analysis. A list of courses along with course syllabi are available on request.ROI Assessment of Technology Investments in the Health Insurance IndustryUsing Monte Carlo simulation, developed innovative approach for ROI assessment of IT investments in software engineering projects where uncertain parameters require probability (stochastic) estimation. Research extension applied a real options framework for structuring IT investment decisions.Using Technology Integration for Improving Firm-level Performance in the Group Health Insurance IndustryInvestigated the impact of automating the exchange of health care enrollment, eligibility and claim data between insurers and medical providers on insurers' KPIs such as employee headcount, claim processing time and claim error rate. Utilized factor analysis for data reduction objective and applied hierarchical / stepwise linear regression to control for confounding effects on target (dependent) variables.A Trial of Smart Card Technology for Payment Settlement in Retail BankingExamined the reasons for a failed smart card trial, launched in 1997-98 by financial service firms and banks in New York City. Used survey method for data collection, 'simultaneous linear regression' model for hypotheses testing, and variance inflation factor (VIF) test to assess model stability.