For the given job description <<The Senior Manager, Data Architecture & Data Engineering is responsible for translating business strategic goals into future-state data engineering and data science solutions. This includes delivery full life-cycle from execution roadmaps, to implementation, and to production operations. This role drives the creation and maintenance of enterprise data and analytics capabilities, data reference architectures, and integrated data & analytics technology roadmaps that will guide our strategic investments and enterprise strategy.Translate business strategic goals into actionable future-state data engineering and data science architectures and solutions.Delivery of full life-cycle Data Solutions from execution roadmaps, to implementation, and to operations. This includes a specific focus on ELT and ETL Data Pipeline solutions from inception to production.Lead end to end solution delivery of enterprise data warehouse, data marts, and data lake with governance.Drive the creation and maintenance of enterprise data and analytics capabilities, data solutions, and integrated data & analytics technology roadmaps that will guide our strategic investments and enterprise strategy.Deliver architecture connecting Data Science, Data Engineering, and Data Analytics practices together to deliver impactful data products.Strong partnership with DevOps, DataOps, business partners, engineering teams, and data management.A minimum of ten (10) years of experience in IT with a minimum (6) years in data architecture and data solutions are required.Successful track-record in managing a team of Data Engineers and Data ScientistsSkilled in hybrid cloud data platforms and analytical technologies including Azure andor GCP data infrastructure, Spark, Data Lake, Data Warehouse and Data Marts, BI reporting with PowerBI, Metadata Management, Data Governance, Master Data Management, Data Catalog, and Business Glossary.Ability to document, communicate, implement, and operationalize complex architectures.Experience with ELT and ETL Data Pipeline solutions from inception to production.Proficient in SQL, NoSQL and columnar databases.Experience with SparkPyspark and JSON.Excellent grasp of Computer Science fundamentals - data structures and algorithms.Must have Panda Data Frames knowledge and best practices, execution engines, DAGs (airflow, dagster, luigi, etc), and Kubernetes.Proficient with GitLabFamiliarity with Azure data factory preferred, Google data flow or AWS GlueExperience with Biomedical Informatics, S3Azure blob store, Machine Learning and AI, Unstructured Data, and Data Democratization is preferred.Experience with Agile methodologies, Kanban preferred, and using tools like Jira and Confluence to facilitate the work.Bachelor's degree in computer science or a related discipline required. Master's degree preferred. RECRUITERS NOTES: The role will definitely be hands-on and will require excellent communication skills. The hiring manager mentioned the following technologies: AImachine learning; data warehouses; product support; Cloud experienceAzure; must be open to new technologies. Working with the Product Development Team. Act as the point of contact; will create a group that helps the product teams. Create data warehouses for analytics. MUST have excellent communication skills  verbal and written. This person will envision strategy and must be able to effectively communicate that strategy to others.  The organization has asked me not to advertise the compensation. If you contact me I would be more than willing to share it with you. 
For over 50 years this non-profit organization has made it their mission to protect patients from unsafe and ineffective medical technologies and practices. More than 5,000 healthcare institutions and systems worldwide including four out of every five U.S. hospitals rely on the services of this organization to guide their operational and strategic decisions. In addition, they serve public and private payers, federal and state agencies, policymakers, ministries of health, associations, and accrediting agencies. Benefit from a healthy work-life balance while staying on the leading edge of technology and thriving in an innovative startup-like culture minus the risk. Sleep well knowing you are helping achieve a world where safe, high-quality healthcare is accessible to everyone. Excellent benefits to include work from home, flex-time and 19 days starting vacation.
Its a nice place to work!>> the resume: <<Summary12+ years of machine learning/data science experience with a proven track record of developing and
       implementing large scale algorithms that have significantly impacted business revenues and user experience.
       10+ years of experience applying R, Python, SAS and SQL for algorithm development, data modeling, statistical
       learnings and data visualization.
       Ranked a top 1% competitor on Kaggle (worlds largest community of data scientist with over 300k members)
       Hands-on experience applying several ML/Statistical algorithms to real-world problems: Neural Networks,
       Gradient Boosted Trees, Random Forest, Clustering, Generalized Linear Models, Simulation models and
       Gaussian Mixture Models
       Strong experience building end-to-end machine learning platform using Java and big data technologies
       (Cassandra, Spark and Hive).
       Excellent skills at programming in Java/OOP
HighlightsR          Neural NetworksJava          Gradient Boosted TreesPython          Random ForestNoSQL, Cassandra          Generalized Linear ModelsSpark, Hive, Pig          OptimizationSQL, CQL          Gaussian Mixture Models
Experience04/2017toCurrentSenior Data ScientistFactset Research Systems Inc.–Reston,VA,Conceptualized, architected and implemented a deep learning model to accurately predict demand for Nvidia's products. Algorithm applies Deep neural networks along with several real time social feeds, econometric indicators, product momentum to forecast demand.06/2014to04/2016Data Science LeadBristol Myers Squibb–Minneapolis,MN,Developed and owned the core machine learning feature of the product, a patent-pending behavioral analytics engine for
predicting cross-cloud application security threats.Architected and implemented the machine learning platform from the
ground up in Java, Cassandra, Hive and Spark.Analytic platform monitors multiple cloud applications (AWS, Salesforce,
Box etc.) and predicts security threats and breaches.Implemented the end-to-end platform for performing user behavior analytics using unsupervised machine
        learning.Designed and implemented algorithms for real-time decisioning
        Implemented process to capture 500+ behavioral profile features
        Implemented several high IV features for ML algorithm consumption: (Maximum Geo-distance between users
        login/day, Fast Geolocation Hopping etc.)
        Owned the feature end-to-end and worked directly with co-founder/CTO to drive product enhancements.12/2012to05/2014Principal Data ScientistLumeris–Saint Louis,MO,Lead several big data machine learning initiatives involving the design, development and deployment of advanced
machine learning algorithms that impacted PayPal's core products allowing business to grow and scale to over 150
million customers and process over 3 billion online transactions annually.The developed solutions have directly helped
PayPal lower losses, improve user experience and increase revenue simultaneously.Few key PayPal core products
worked on are included below.PayPal ATO Models:
        Designed and developed Neural Network models to prevent account takeover activity at the time of user
        authentication.Performed feature selection from over 2000 features, and applied best practices in Neural Network
        model development.Model accurately captured 81% of incoming fraud activity and resulted in 48bps of reduction
        of losses for PayPal in 2013 freeing up capital to grow and expand margins.PayPal Here Mobile Card Reader:
        Architected the very first anti-fraud ML algorithm that enabled PayPal here product to expand and scale to over
      million merchants.Designed many new geolocation specific features that showed high IV contribution to the
      implemented model.PayPal Wallet:
      Spearheaded the development of ensemble models for predicting stolen financial usage at the time of transaction
      completion.Developed Gradient Boosted Trees using GBM package in R.Implemented new features using IP,
      IP-GEO, Velocity etc.to improve algorithm performance.Designed several tools in R to visualize GBT model and
      developed process to extract model parameters for deployment.Partnered with business and product teams to
      execute and influence business decision around the solution implementation.05/2007to11/2012Lead Analytic ScientistCushman & Wakefield Inc–Stockton,CA,Managed the design, development and deployment of several predictive modeling solutions that underlie ISO Risk
Analyzer portfolio of products which have contributed to significant revenues for the company.Few highlights are listed
below.Lead the development of an expert system to accurately predict a consumer's likelihood of committing a traffic violation.Developed Logistic models for predicting violation probability and Poisson models for predict violation frequency.Incorporated intelligence from 1500 features from 7 different data sources (Claritas, Crime, Traffic Patterns, Business Info, Weather etc.) Tweedie/Compound Poisson Distribution Model Developed and implemented several Tweedie distribution models to examine environmental factor for predicting personal auto risks for calculating insurance premiums.Developed separate frequency and severity models to improve model accuracy.Captured interaction effects between various environment variables such as traffic generator, traffic pattens, population density, weather etc.Risk Analyzer Credit Scoring Module: Designed and developed first generation credit scoring algorithms to predict a consumer's accident likelihood using credit payment behavior.Performed feature reduction on close to 800 credit attributes and applied Multivariate Adaptive Regression Splines (MARS) to detect 2nd and 3rd order interactions effects.Captured non-linearity using splines and piecewise regression.Worked as the analytic manager for the movie advertisement testing product, a high revenue growth initiative for the company.09/2004to04/2007Senior Research AnalystNbc Universal–Boston,MA,Managed 5 statisticians to build predictive models for rating movie trailer/TV spots content on their
effectiveness to generate interest.Provided analytic insights and advice to maximize the impact and reach of client's
resources.The product served 4 of the top hollywood movie studios and generated over $13M in annualized revenues for
the company.Movie Trailer/TV Spots Advertisement Testing Product (Predictive models for rating movie trailers, TV spots, print ads
etc.).Team worked on over 100 movies for most of the motion picture studios-majors, and assisted in building
        customized statistical models for several mini-majors.Provided strategic insights and advice to Hollywood
        Studios to maximize the impact and reach of clients marketing resources.Box Office Forecasting Models:
        Developed forecasting models to predict weekend box office revenues to help studios estimate accurately a films
        potential at the day of the release.Model used several audience tracking data to accurately predict weekend box
        office receipts.Fox Movie Sequel Study (Client - 20th Century Fox) ?
        Developed a series of ordinal logistic regression models to compare characteristics and attributes describing
        several Fox movies.Based on the study results, 20th Century Fox decided to produce a sequel to Die Hard in
        2007, which was one of the biggest box office hits of that year.01/2002to01/2004ResearcherUniversity Of Missouri–City,STATE,Managed home healthcare initiative for Missouri's Sinclair School that involved developing a AI system based GPS to
optimize hospital resources for saving cost.The GPS system incorporated new algorithms for solving multiple-traveling
salesman problem in a dynamic setting.Researched and applied spatial clustering algorithms in the context of
constraint optimization problem.
EducationExpected in2004totoMaster of Science:Industrial Engineering/Operations ResearchUniversity of Missouri-Columbia,MOGPA:Industrial Engineering/Operations Research Thesis - "Dynamic Stochastic Vehicle Routing Model in Home Healthcare Scheduling"Expected in2002toto:Metallurgical EngineeringIndian Institute of Technology-,GPA:Metallurgical Engineering BHU          Varanasi, UP, IN
Thesis - "Monte Carlo Simulation Modeling of Non-Uniform Stress in Structured Materials"
{Secured All India IITJEE rank in the top 0.6% -- ranked among 250,000 applicants}
Skillsads, AI, big data, clustering, content, Credit, Client, clients, expert system, Fast, features, financial, Forecasting, GPS, Home Healthcare, insurance, IP, ISO, Java, machine learning, machine
        learning, marketing, MARS, Materials, Office, 2000, Modeling, monitors, Network, Networks, Neural, NoSQL, Optimization, predict, Testing Product, Python, real-time, Routing, Scheduling, Simulation, SQL, strategic, Structured, TV>>. The result is, No Fit

Fit assessment: No Fit