Michelle robbins - impact driven: decision science

Transcript

1. Harnessing Decision Science for Tech Enterprises Michelle Robbins Manager, Decision Science Turing Fest - June 28, 2023
2. Michelle Robbins Decision Scientist Analyst | Engineer | Board Member Caffeine-based lifeform
4. Decision Science Where interdisciplinary fields come together
5. Decision Science Where interdisciplinary fields come together
6. The Role of Decision Science • Strategic Decision-Making • Optimizing Business Processes
7. The Role of Decision Science • Strategic Decision-Making • Optimizing Business Processes • Risk Assessment • Data Analysis & Interpretation
8. Leveraging Decision Science A comprehensive landscape of methods, tools and techniques • Advanced Analytics • Predictive Modeling
9. Leveraging Decision Science A comprehensive landscape of methods, tools and techniques • Advanced Analytics • Predictive Modeling • Data Visualization • Decision Modeling • Optimization Techniques
10. Leveraging Decision Science A comprehensive landscape of methods, tools and techniques • Advanced Analytics • Predictive Modeling • Data Visualization • Decision Modeling • Optimization Techniques • AI and Machine Learning
11. Case Studies
12. Strategic Decision Making at Netflix Content Recommendations, Investments and Creation
14. Content Decisions What to produce?
16. Content Decisions How much money to invest?
18. Decision Science at Amazon • Cost Optimization • Logistics • Supply Chain Management • Dynamic Pricing Strategy • Inventory Management • Transportation Delivery Network • Fulfillment Centers
24. Airbnb: Risk Mitigation
27. By leveraging analytical tools, and predictive models, these tech companies were able to overcome significant challenges and achieve impressive results.
28. Decision Science and Start- ups
29. Decision Science and Start- ups
30. Operational Optimization • Product Development • Customer Acquisition • Customer Retention
31. Operational Optimization • Product Development • Customer Acquisition • Customer Retention • Talent Acquisition • Talent Management
32. Operational Optimization • Product Development • Customer Acquisition • Customer Retention • Talent Acquisition • Talent Management • Financial Planning • Resource Allocation
33. Operational Optimization • Product Development • Customer Acquisition • Customer Retention • Talent Acquisition • Talent Management • Financial Planning • Resource Allocation • Risk Management
34. Case Study: Stitch Fix Growth through Advanced Analytics • Personalization Algorithms • Collaborative filtering • Content-based filtering
35. Case Study: Stitch Fix Growth through Advanced Analytics • Personalization Algorithms • Collaborative filtering • Content-based filtering • Inventory Management • Stylist Selection
37. Scale-ups and Decision Science Unlock Rapid Growth
38. Scale-ups and Decision Science Unlock Rapid Growth
40. Data-driven Decision Making The key to operational efficiency and intelligent scaling
41. Case Study:Uber Rapid growth and transformation Dynamic Pricing Route Optimization Market Expansion Driver Retention
47. Building Your Team Creating an end-to-end Decision Science Discipline
48. Set a Strong Foundation Build with intention, index on immediate needs, iterate and continually develop your talent • Define clear roles • Identify necessary skills
49. Set a Strong Foundation Build with intention, index on immediate needs, iterate and continually develop your talent • Define clear roles • Identify necessary skills • Prioritize a strong domain background • Value relevant experience
50. Set a Strong Foundation Build with intention, index on immediate needs, iterate and continually develop your talent • Define clear roles • Identify necessary skills • Prioritize a strong domain background • Value relevant experience • Build a culture that values data-driven decision making • Invest in continued training and development
51. Set a Strong Foundation Build with intention, index on immediate needs, iterate and continually develop your talent • Define clear roles • Identify necessary skills • Prioritize a strong domain background • Value relevant experience • Build a culture that values data-driven decision making • Invest in continued training and development • Foster diversity
52. Hiring for an end-to-end decision-focused data organization Full-Stack Data Team Roles and Responsibilities Data Engineer - Develop, construct, test, and maintain architectures (cloud, large-scale data systems/warehouses) - Extract, transform, and load data from various sources into the data platform. - Create and manage data pipeline architecture and structure the flow of data from various sources. Data Analyst - Collect, process, and interpret complex data to derive actionable insights. - Develop, manage, and maintain databases, data systems, and reports. - Identify, analyze, and interpret trends or patterns in complex data sets. Data Scientist - Data mining using state-of-the-art methods. - Select features, build and optimize classifiers using machine learning techniques. - Implement new statistical or other mathematical methodologies as needed for specific models or analysis. Statistician - Apply statistical theory to solve real- world problems, often in the context of making decisions in an uncertain environment. - Develop, validate and maintain statistical models. - Explain complex statistical concepts and analysis outcomes to non-statistical stakeholders. Decision Scientist - Identify and understand business problems to improve decision making. - Conduct quantitative analysis and modeling of business scenarios. - Collaborate with other departments to ensure implementation of strategic. - Define the metrics used to monitor and predict performance
53. SQL Python/R Stats Eng. ML AI Data Engineer Data Analyst Statistician Data Scientist Decision Scientist Full-Stack Data Team Core Competencies *
54. Technical, Educational and Experience-based skills Full-Stack Data Team Core Competencies Engineering Data Engineer only/primarily: - Scaled data infra (Azure, GCP, AWS, Hadoop, Oracle, IBM) - ETL/ELT (Azkaban, Airflow, etc.) - ML/AI tooling and infra (Azure Data Factory, Google Cloud AI, IBM Watson, etc.) SQL Core capability for all team members • Data engineer • Data analyst • Statistician • Data Scientist • Decision Scientist Statistics Core capability for most team members • Data Analyst • Statistician • Data Scientist • Decision Scientist Python / R Core capability for most team members • Data engineer • Statistician • Data Scientist • Decision Scientist Nice to have: • Data Analyst ML / AI Core capability for some team members • Data Scientist Nice to have: • Decision Scientist • Statistician
55. What’s Next? Innovation in the field of Decision Science
56. Thank you! Join me for the roundtable Q&A @12:50 Michelle Robbins Manager, Decision Science June 28, 2023