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Skills

  • Operational improvement
  • Team management
  • Customer Churn Analysis
  • Administrative support
  • Organization
  • Risk assessments
  • Insurance billing
  • Big Data tools and technologies (e.g., Hadoop, Spark)
  • Data pre-processing and cleaning
  • Planning and coordination

Work Experiences

  • Analyzed customer engagement data.
  • Kept my initiative knowledge up to date by doing strategic planning and vendor management.
  • Collaborated with C-level executives.
  • Increased profit margins by streamlining operations and workflow and negotiating competitive vendor contracts.
  • SAP was delivered to customer locations by me.
  • Provided excellent service and attention when dealing with customers face-to-face or over the phone.
  • Site evaluations, customer surveys, and team audits were all things I did.
  • Collaborated on grant funding initiatives, providing data projections that helped secure $9 for future machine learning research funding in 2019.
  • Checked prototypes and finished products for quality and design specifications.
  • Worked with others to brainstorm new metric possibilities.

Summaries

  • Project and team leader with extensive experience in conceptual design and product implementation support.
  • Motivating leader who has built and managed program teams in the past.
  • Student pursuing a Biotechnology degree who is eager to contribute developed knowledge in the role of Lead Data Scientist.
  • Capable of assessing risks, resolving problems, and conducting product testing.
  • Exceptional at juggling multiple goals in order to maximize efficiency and influence positive outcomes.
  • Working with stakeholders and corporate teams is a breeze for this strong relationship builder.
  • Achieved 82% growth in profitability during 2018.
  • Communication, project planning, and organizational skills are all important.
  • Lead Data Scientist with data analysis and strategic planning abilities.
  • Proven track record of motivating automated to achieve team, individual, and management goals.

Accomplishments

  • Mentored a team of junior data scientists and engineers, resulting in an overall 83% boost in team performance and project throughput.
  • Executed an ensemble machine learning approach by combining multiple models which led to a 25% improvement in method accuracy.
  • Optimized a recommendation engine in procedure, improving user engagement and increasing click-through rates by 36% over 9 months.
  • Developed predictive models for demand forecasting using machine learning algorithms, which reduced stockouts by 47% for Workday.
  • Integrated APIs for external dataset access, superiorly enhancing model capabilities and improving product performance by 42% upon deployment.
  • Directed the implementation of data governance standards across the company, minimizing data discrepancies and improving data reliability by 48%.
  • Spearheaded the migration to a scalable cloud-based data warehouse, reducing ETL processing time by 40% and improving data access for cross-functional teams.
  • Addressed technical debt by refactoring legacy code, reducing model inference times by 81% and improving overall maintainability.
  • Developed and deployed a machine learning model for fraud detection that increased accuracy by 41%, reducing fraud-related costs by $11 per quarter.
  • Collaborated with 5 stakeholders to integrate machine learning tools into business decision-making processes, leading to a 21% improvement in operational efficiency.

Affiliations

  • Contributor, The Journal of Artificial Intelligence Research - Published thorough case studies on agile project management in machine learning implementations.
  • Member, Society for Industrial and Applied Mathematics (SIAM) - Special focus on applied machine learning methodologies.
  • Co-Chair, Machine Learning: Running and Scaling Solutions in Denver - Organized discussions and professional panels on scalable machine learning architectures.
  • Participant, Global AI Ethics Consortium - Contributed to frameworks ensuring machine learning compliance with privacy regulations such as framework.
  • Certified Machine Learning Specialist, Open Source Machine Learning Institute - Completed certifications and advanced courses on clustering and pre-processing techniques for large datasets.
  • Fellow, Data Science Innovation Alliance - Collaborated on machine learning innovations for operational efficiency across geographically disperse regions.
  • Mentor, Data Science Central - Facilitating workshops and mentoring aspiring data scientists on building automated machine learning pipelines.
  • Member, International Association for Statistical Computing (IASC) - Leading discussions on hybrid machine learning applications for federated data algorithms.
  • Member, Algorithmic Accountability Association - Engaged in discussions on the ethics and transparency of AI-driven decision-making systems.
  • Member, Tech Innovation Membership Program - Focused on predictive analytics in high-transactional environments.

Certifications

  • TensorFlow Advanced Techniques Specialization by Coursera
  • Hadoop Big Data Certification by Cloudera
  • Machine Learning Certification by Stanford University (Coursera)
  • SAS Certified Data Scientist
  • Big Data Hadoop Administrator by Simplilearn
  • First Aid/CPR
  • Google Certified Professional Cloud Architect
  • Certified Specialist in Predictive Analytics (CSPA) by The Institute for Statistics Education
  • Neo4j Professional Certification in Graph Algorithms
  • Microsoft Certified Systems Engineer (MCSE)

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