Paul McLeod

Principal @Decision Operations

Berlin, DE
MOBILE NUMBERS
+91 *********19

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WORK HISTORY

Aug 2015 — Present

Principal @Decision Operations

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Smoother, faster, more -decisive- business operations. Putting computational analytics to work in repetitive complex business operations. People define the policy, machines do the math.These Analytics make a -direct- difference for your people, your customers and your bottom line.High volume Decisioning. Analytical Decision Management. Prediction. Optimization. Prescriptive Analytics. Complex Event Processing. Early Warning Systems.Systems for Data Science Team Enablement.Decision Centered Consulting, Decision Modeling Notation, etc.Techniques: Neural Nets, Jitter, Logistic/Linear Regression, RFM, DecisionTrees/Rules (RandomForest/CHAID/Quest/CART), Boosting/Bagging, SVM, Clustering / KNN, CARMA/APRIORI, Temporal Entities, ARIMA, MIP/LP, Network/Connectedness Analytics (\'betweenness\', density), Space-Time boxes, Geospatial Centricity/Reverse Geocoding, Feature engineering, ABC, Anomaly analysis, Bins/Tiles Interactive visualization & dashboarding.Domains: Predictive Maintenance. Distribution / Demand Planning. Marketing / Campaigns. Operational Planning, Scheduling, Student success, Transportation, Trading, Lending Fast-tracking.email: p••••••••@decisionoperations.com

EDUCATION

N/A

Sydney Conservatorium of Music

Classical Voice/Opera (Extenaion Studoes)

1991 — 1994

UNSW

BSc (Comp Sci), Data Management, User Oriented Design

SKILLS

Machine LearningBusiness ProcessBusiness IntelligencePerformance ManagementStrategyIntegrationPredictive AnalyticsData ScienceRetailBusiness CaseData ModelingInternet of ThingsData MungingEtlData WarehousingDecisioningData PreparationCloud ComputingData IntegrationBusiness AlignmentGamificationCplexBig Data AnalyticsDesign ThinkingNew Business DevelopmentAnalyticsPythonLateral ThinkingCustomer Experience TransformationPredictive ModelingLateral SolutionsOlapRequirements GatheringInformation ModelingDistributionDimensional ModelingAutomationDiverse Data TechniquesData Warehouse ArchitectureTechnology Trends

ABOUT PAUL MCLEOD

I\'m an Architect experienced at getting Enterprise results with Artificial Intelligence, Automation, Data & co.I exploit emerging tech, designing & delivering quality Analytic Solutions that make Businesses and Brands more efficient in their operations, more true to their goals, and more attentive to their customers\' needs.I help organizations harness the power of Data (of all variety) in their moment-by-moment market-facing operations. A business which connects fully with Data can act decisively, without hesitation, and thrive on scale. My key discipline, Decision Management, is about putting data to work, mapping analytics to action.It can be seen as the practical side of AI and Data Science. the part measured by business impact.Expertise includes: Information Management (the x Vs), Data Engineering, Machine Learning, AIs / LLMs, Deep Learning, Data Science / Advanced Analytics, Operations Research, Cognitive Systems, Business Rules, Streaming Data, Data APIs, and Automation. I’ve been increasingly hands-on with some emtech like IoT, Telepresence, Wearables, Mixed Reality, Robotics, Virtual Assistants and Affective Computing. which are becoming relevant in ‘closing the loop’ of automation/engagement.Cloud / Microservices / PaaS / in-browser is my normal idiom and I know my way around the zoo of New-World data technologies (Schema-less, Streams, Functional, Mesh, Lakes, Reservoirs) – I started early with these. At the same time, I’m no stranger to the older world of BI/DW/SQL/OLAP.This work involves all kinds of stakeholders (business, technical). I also teach courses, and I run comprehensive Review exercises on a business\' Platform trajectory. Modelling applications include: costing/profitability; optimization, attrition/churn; market basket analysis; predictive analytics (Predictive Maintenance, Asset Management); gamification/incentivation; travel; hyperpersonalization and statutory/compliance. Languages, Frameworks, etc: python/Jupyter/Zeppelin, SPSS, Watson, NodeRED, ROS/Edison/LoRaWAN, Swagger/OpenAPI Spark/Hadoop/YARN, XML/Xpath/XSLT, JSON/JSONata, SQL, DMN, node.js/es6, A-Frame, prolog, java, SED/Regexp, etc.I’ve been one of the earliest proponents of secure data analytics in the cloud, and I\'ve a long history of success with designing and delivering robust scalable platforms for high-volume data analytics in large organisations.I\'ve nearly 30 years experience in analytics technologies, and yes AI (since prolog days) and have consulted extensively across Australia/NZ, Asia and Europe, where I am now based.

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Paul McLeod — Principal at Decision Operations in Berlin, DE | Unifers