1. Math in Business Cathy O'Neil mathbabe.org

2. Outline of talk

3. What are the options?

4. Outline of talk What are the cultural differences?

5. Outline of talk What are the mathematical differences?

6. Outline of talk Typical data scientist duties

7. Outline of talk Ethics, and how to make the world a better place

8. What are the options?

9. Working as an academic mathematician

10. What are the options? Working at a government institution

11. What are the options? Working as a quant in finance

12. What are the options? Working as a data scientist

13. Cultural Differences

14. Feedback is slow in academics

15. Cultural Differences Institutions are painfully bureaucratic

16. Cultural Differences Finance firms are cut-throat

17. Cultural Differences Startups are unstable

18. Cultural Differences Outside academics, mathematicians have superpowers

19. Cultural Differences Inside academics, you get more flexible hours and summers off (!?)

20. Cultural Differences Outside academics, you get rewarded for organizational skills (punished within)

21. Cultural Differences Academic freedom is awesome but can come with insularity

22. Cultural Differences You don't decide what to work on in business but the questions can be really interesting

23. Cultural Differences You can't share proprietary information with the outside world when you work in business or for the government

24. Cultural Differences On the other hand, sometimes you can and it might make a difference

25. Cultural Differences In business, more emphasis on shallower, short term results

26. Cultural Differences On the other hand, you get much more feedback

27. Cultural Differences As in research, you learn tools and apply them

28. Cultural Differences You have to constantly be aware of the business context (which can be good)

29. Mathematical Differences

30. Quants in finance usually come from math and physics, data scientists come from stats and CS

31. Mathematical Differences In academics the data is small In finance it’s medium In data science it’s big

32. Mathematical Differences In finance signal is tiny In data science it’s big

33. Mathematical Differences Finance: time series Machine Learning: pile o’ data

34. Mathematical Differences Seasonality really matters (not user attributes as much as user behavior)

35. Mathematical Differences In finance, can change frequency of data to compress models - but not in user modeling

36. Mathematical Differences The concept of exponential decay of signal is sacrosanct in finance

37. Mathematical Differences Thus online learning

38. Mathematical Differences Bayesian priors (two versions): generalized smoothness assumptions

39. Mathematical Differences Questions academics focus on seem weirdly specific

40. Mathematical Differences Questions academics focus on seem weirdly specific (no offense)

41. Mathematical Differences Finance uses mostly linear regression for forecasting (sometimes with trees)

42. Mathematical Differences Machine learners have all sorts of cool models (a question of accuracy)

43. Mathematical Differences The hardest things to do probably have small signal (or complicated relationships)

44. Typical data scientist duties

45. You spend time visualizing the data for the sake of non-quants

46. Reporting help

47. Typical data scientist duties You spend time modeling (forecasting user behavior)

49. Typical data scientist duties You spend time monitoring signal versus noise

50. Typical data scientist duties You spend time on business development

51. How do I get a job like that?

52. Get a Ph.D. (establish your creativity)

53. How do I get a job like that? Know your way around a computer (awk grep “rm –fr *”)

54. How do I get a job like that? Learn python or R, MapReduce or pig

55. How do I get a job like that? Get some domain knowledge (vocabulary)

56. How do I get a job like that? Acquire some data visualization skills (knowing what is crucial)

57. How do I get a job like that? Learn basic statistics

58. How do I get a job like that? Read up on machine learning

59. How do I get a job like that? Emphasize your communication skills and follow-through

60. How do I get a job like that? Practice explaining what a confidence interval is

61. Ethics, and how to make the world a better place

62. Other stuff Data modeling is everywhere (good data modelers aren't)

63. Other stuff Beware the authority of the inscrutable

64. Other stuff Open source data, open source modeling

65. Other stuff Modeler’s Hippocratic Oath

66. Other stuff Meetups

67. Other stuff Data hackathons

68. Other stuff Data science for the rest of us?