The data scientist identity - chaire PARI › wp-content › uploads › 2019 › ... · • 2012:...

Reflexive creativity in the digital age

The data scientist identity

Séminaire de la chaire PARI

Philipp Brandt Sciences Po

October 16, 2019

1

2

Plan• Data science

Unconventional work with data

• Data science in historyNew approaches in quantitative fields

• Data science of data scienceExperimenting with novel skill combinations

• Professional identity from reflexive creativityNew boundaries around old ideas and familiar problems

3

Data scienceRachel Schutt, a senior research scientist at Johnson Research Labs,…: “a hybrid computer scientist software engineer statistician. … The best tend to be really curious people, thinkers who ask good questions and are O.K. dealing with unstructured situations and trying to find structure in them.”

4


5


6


7

Data science

“Data Scientist” in U.S. newspapers; Source: Factiva

Rachel Schutt, a senior research scientist at Johnson Research Labs,…: “a hybrid computer scientist software engineer statistician. … The best tend to be really curious people, thinkers who ask good questions and are O.K. dealing with unstructured situations and trying to find structure in them.”

8

Data science

9

Professional identity

Professions and Expertise

10

How have data experts constructed a novel one?

11

Professional identities are plentiful.

How have data experts constructed a novel one?

12

Professional identities are plentiful.

Reflexive creativity!

History

• 2012: “The sexiest job of the 21st century” (Davenport and Patil)

• 2005: “Data science as an academic discipline” (Smith)

• 2001: “Data Science: an Action Plan for Expanding the Technical Areas of the Field of Statistics” (Cleveland)

• 1998: “What is Data Science?”(Hayashi in Data Science, Classification, and Related Methods)

13

History





14

History





15

History





16

HistoryScientific impurity

Time

Statistics

Data science

Practical discourse

Social sciences

Classification Association

CODATA

1996 201217

Data science of data science

18

Data science jobsYou will handle data exploration, hypothesis creation (from both business and product goals), testing algorithms, scaling to large data-sets and validating results. We have a broad set of technologies with which the Senior Data Scientist will work: Hadoop/HDFS; Shark/Spark; NoSQL databases, and numerous charting, graphing and analysis applications such as: Gephi, Google Charts, etc. [...] We’d like to see good coding skills covering some procedural as well as statistical or data oriented languages. (Such as: Java, Scala, Python as well as R, SQL, etc.). Good communication skills and an awareness of how to communicate data effectively is a must. This individual must be comfortable working in newly forming ambiguous areas where learning and adaptability are key skills . Required Education: MS or higher in the field of Statistics or Computer Science or Applied Mathematics.

19

Data science jobsYou will handle data exploration, hypothesis creation (from both business and product goals), testing algorithms, scaling to large data-sets and validating results. We have a broad set of technologies with which the Senior Data Scientist will work: Hadoop/HDFS; Shark/Spark; NoSQL databases, and numerous charting, graphing and analysis applications such as: Gephi, Google Charts, etc. [...] We’d like to see good coding skills covering some procedural as well as statistical or data oriented languages. (Such as: Java, Scala, Python as well as R, SQL, etc.). Good communication skills and an awareness of how to communicate data effectively is a must. This individual must be comfortable working in newly forming ambiguous areas where learning and adaptability are key skills. Required Education: MS or higher in the field of Statistics or Computer Science or Applied Mathematics.

20

Data structure

21

Job title Time N (in 1,000)Attorney 2013 2.9Data scientist 2013 1.8Software engineer 2013 8.4Random 2010-2013 ~40Source: LinkedIn API; US jobs

Data structure

22

Job title Time N (in 1,000)Attorney 2013 2.9Data scientist 2013 1.8Software engineer 2013 8.4Random 2010-2013 ~40Source: LinkedIn API; US jobs

Analytical pipelineRandom

skill 1 skill 2 …

Data scientist

skill 1 skill 2 …

Random

skill 1 skill 2 …

Data scientist

skill 1 skill 2 …

Random

skill 1 skill 2 …

Data scientist

skill 1 skill 2 …

Random

skill 1 skill 2 …

Data scientist

skill 1 skill 2 …

Random

skill 1 skill 2 …

Data scientist

skill 1 skill 2 …

Random

Blah bla skill 3 bla skill 4 bla …

Data scientist

Blah bla skill 1 bla skill 2 bla …

S1, S2, …, Title

1, 0, …, 1 1, 0, …, 1

1, 0, …, 1 0, 1, …, 0

1, 1, …, 0

Pr(yi=1) = logit-1(Xiβ)

23

Performance

24

Professional recognition

25

Sociological puzzle

26

• Employers associate data scientists with a distinct skill set

• But: this skill set has formed independent of the data scientist label

Professional identity from reflexive creativity

27

Concepts• Structures

Language, capitalism, states (Sewell 1992)Technological objects (Becky 2003; Bowker 2006)Professional rhetorics (Fine 1996; Suddaby and Greenwood 2005)

• Relations Commit, mediate, select (White 2008)Economic advice (Vedres and Stark 2010; Burt 1987)Professional conflicts & expertise networks (Abbott 1988; Eyal 2013)

• PracticeNarrative identity construction (Somers 1994)Creativity and the self (Dewey 1929; Mead 1934; Joas 1994)Provisional selves of professionals (Ibarra 1999)

28

Reflexive creativity

• “Creative action [builds on] pre-reflective aspirations towards which the reflection on the concretization of values is oriented.”

29

(Joas 1996:163)

30

“just one term … to get those annoying research scientists

to do some real work …”

Jeff Hammerbacher Data scientist

Qualitative design

• Field observations between 2012 and 2015

• “Meet-ups” and public events in New York City

• Open Statistical Programming, Data Driven NYC, New York Data Science, MongoDB meet-up

• 70+ Events, 100+ speakers

31

34

–Claudia, advertising

“By the way, you really, really, really don’t want me to optimize clicks. … if you ever want to have a great

click-through campaign, all you need to do is to show the ad on the flashlight app. … It’s a whole bunch of

people fumbling in the dark. They will click on it eventually.”

–Claudia, advertising35

“So ultimately what you need is something I call alternative histories, or counterfactual .…”

36

–Jake, data science institute

- Attendee

“for any of you [in the audience] who are hiring data scientists …, it is really important that you get clear on which one of these buckets you predominantly fit in.”

“… part of the value your graduates could add would be looking at the big picture and saying

look, here is where I could add value .…”

37


- Attendee


“for any of you [in the audience] who are hiring data scientists …, it is really important that you get clear on which one of these buckets you predominantly fit in.”

“… part of the value your graduates could add would be looking at the big picture and saying

look, here is where I could add value .…”

“And the best way is actually … just to come up with very specific examples, …”

38


- Attendee

–Rachel, media company39

“.. we talk to the people in the company who are in charge of the business systems about what data that they have and what data we can have access to. They

often speak in terms of cubes and enterprise data warehouses. And so we wanna get to the raw logs.”


“.. we talk to the people in the company who are in charge of the business systems about what data that they have and what data we can have access to. They

often speak in terms of cubes and enterprise data warehouses. And so we wanna get to the raw logs.”


“And often get initial pushback because they want to know ‘why do you want to deal with the messy logs if

we have these nice, clean data warehouses.’”

Different boundaries

“just one term … to get those annoying research scientists

to do some real work …”

42

“This new field is a better academic enlargement of statistics and machine learning than today’s

Data Science Initiatives, …”

43

The data scientist identity

• Reflexive creativity:Data scientists reconcile their initial ideas with challenges to their self-presentations

• Boundaries and relations:Technical ideas and common imageryClients, scientists and peers

• Shaping data science:Professions, academics, intellectuals, intelligentsiaData “hackers” vs. Data do-gooders

References• Abbott, Andrew. 1988. The system of professions: An essay on the division of expert labor. University of Chicago Press.• Bearman, Peter S, and Katherine Stovel. 2000. "Becoming a Nazi: A model for narrative networks." Poetics 27 (2-3): 69-90.• Bechky, B. A. 2003. "Object lessons: Workplace artifacts as representations of occupational jurisdiction." American Journal

of Sociology 109 (3): 720-752.• Bowker, Geoffrey C. 2005. Memory practices in the sciences. Vol. 205. Mit Press Cambridge, MA.• Burt, Ronald S. 1987. "Social Contagion and Innovation: Cohesion versus Structural Equivalence." American Journal of

Sociology 92 (6): 1287-1335.• Collins, H. M., and Robert Evans. 2007. Rethinking expertise. Chicago: University of Chicago Press.• Dewey, John. 1929. The quest for certainty: a study of the relation of knowledge and action.Gifford lectures, 1929. New York:

Minton, Balch.• Eyal, Gil. 2013. "For a Sociology of Expertise: The Social Origins of the Autism Epidemic." American Journal of Sociology 118

(4): 863-907.• Ibarra, Herminia. 1999. "Provisional selves: Experimenting with image and identity in professional adaptation." Administrative

science quarterly 44 (4): 764-791.• Joas, Hans. 1996. The Creativity of Action. Chicago: University of Chicago Press.• Marres, Noortje. 2017. Digital sociology: The reinvention of social research. John Wiley & Sons.• Mead, George Herbert. 1934. Mind, self and society. Chicago University of Chicago Press.• Salganik, Matthew J. 2017. Bit by bit: social research in the digital age. Princeton University Press.• Sewell, William H., Jr. 1992. "A Theory of Structure: Duality, Agency, and Transformation." American Journal of Sociology 98

(1): 1-29.• Somers, Margaret R. 1994. "The narrative constitution of identity: A relational and network approach." Theory and society 23

(5): 605-649.• Suddaby, Roy, and Royston Greenwood. 2005. "Rhetorical strategies of legitimacy." Administrative science quarterly 50 (1):

35-67.• White, Harrison C. 2008. Identity and control: How social formations emerge. 2nd. ed.: Princeton university press.

45

Higher education

Statistics Applied mathematics Mathematics with a statistics focus

Higher education

Master of Science in Analytics at Louisiana State University’s E.J. Ourso College of Business

Higher education

Master of Professional Studies in applied statistics, data science option (Cornell University)

Higher education

M.S./Ph.D. in Data Science New York University Purdue University

Quantitative design

Job title N (in 1,000)

Attorney 2.9Data scientist 1.8

Financial advisor 1.8

Risk analyst 1.2

Software engineer 8.4

Random ~40

50

Professional recognition

51

Data science

Financial advisors Law

Name Setting Story

Motivation Proposition Presentation

Claudia Advertising Advertising counterfactuals flashlight appAaron Education Education validity guys next doorJake Human rights Violations accuracy We’re not in JordanJohn Digital design Schedule polytopes lunch breaksRiley Accommodation Efficiency data repository democratizes dataRachel Mass media Business raw logs chaos, tactics

Con

sulti

ngC

ontra

cts

Broader pattern

53

Name Setting Story



Con

sulti

ngC

ontra

cts

54

Name Setting Story



Con

sulti

ngC

ontra

cts

55

Name Setting Story



Con

sulti

ngC

ontra

cts

56

Name Setting Story



Con

sulti

ngC

ontra

cts

57

Name Setting Story


John Photo sharing Technique R. A. Fisher titans and blessing

Tristan Analytics platform probabilistic programming

Bayes, von Neumann google it, authority

Hilary Url management OSEMN Chris Wiggins Columbia University

Adam Blogging platform Programming system

John D. Cook programming philosopher

David Video captioning Deep learning Krizhevsky competition, Stanford

Yann Social networking Deep learning Scientists hesitant academics

Hannah Tech research Social problems Social scientists astronomers

Hilary Url management Arab spring researchers experts

Vaclav Online dating Study PNAS top-rated journal

58

Name Setting Story













59

Name Setting Story













60

Name Setting Story













61

Name Setting Story













62

Name Setting Story













63

Name Setting Story













64

Name Setting Story


Michael Digital marketing MapReduce Iterative work Claudia

Jeremy Data science platform MapReduce Pragmatism Claudia

Riley Accommodation Responsibility Fueled by data Square

John Social network Technique No hand-tuning John Langford

Rachel Mass media Skills Coding no scientist or managers

Michael Data science training Skills Recursion coding reviews

Riley Accommodation Hiring Practice Test day

Jake Data science institute Hiring Uncertainty Examples

Imag

inar

yR

eal

65

Name Setting Story










Imag

inar

yR

eal

66

Name Setting Story










Imag

inar

yR

eal

67

Name Setting Story










Imag

inar

yR

eal

68

Name Setting Story








Riley Accommodation Hiring Intelligence IQ tests

Jake Data science institute Hiring Methods Buckets

Imag

inar

yR

eal

69

Name Setting Story








Riley Accommodation Hiring Intelligence IQ tests

Jake Data science institute Hiring Methods Buckets

Imag

inar

yR

eal

70

Name Setting Story










Imag

inar

yR

eal

71

Name Setting Story










Imag

inar

yR

eal

72

The data scientist identity - chaire PARI › wp-content › uploads › 2019 › ... · • 2012:...

Documents

Transcript of The data scientist identity - chaire PARI › wp-content › uploads › 2019 › ... · • 2012:...