Success Profile for Data Analytics Roles
 
Data Scientist Role
 
Snapshot of the data used for analysis

Survey Items Employee
75121, self
rated
Employee
75121, manager
rated
Employee
75122, self
rated
Employee
75122, manager
rated
Employee
10335, self
rated
Employee
10335, manager
rated
Visualization 1 5 5 4 4 5 5
Statistical Analysis 2 5 5 4 5 4 4
Data Gap Identification 3 5 4 4 4 5 4
Analytics Workflow 4 5 5 5 5 4 4
Programming & Coding 5 5 5 4 5 4 5
Data Monetization 6 4 4 3 4 4 5
Programming & Coding 7 2 3 4 5 3 4
Business Intelligence 8 5 4 2 3 4 4
Data Monetization 9 5 4 3 4 4 4
Feature Engineering 10 4 3 4 4 5 4
Business Intelligence 11 4 4 2 4 4 5
Theoretical Quantitative Foundations 12 4 5 5 4 4 4
Analytics Workflow 13 5 4 3 5 5 4
Data Analysis & Interpretation 14 5 4 4 5 5 4
Data Analysis & Interpretation 15 4 4 4 5 4 4
Statistical Analysis 16 3 2 4 5 5 4
Visualization 17 5 3 2 4 4 4
Business Intelligence 18 5 3 2 5 4 4
Model Validation 19 3 4 3 . 4 3
Feature Engineering 20 4 4 3 4 4 4
Munging data 21 5 4 4 5 4 5
Model Validation 22 5 5 4 4 4 4
Tech Savvy 23 . 3 4 4 4 4



Analysis of Agreement Among Raters
 
Agreement/Disagreement on Items

# Item Agreement with Manager
1 Business Intelligence 11 In Agreement
2 Data Analysis & Interpretation 15 In Agreement
3 Data Gap Identification 3 In Agreement
4 Data Monetization 6 In Agreement
5 Data Monetization 9 In Agreement
6 Feature Engineering 10 In Agreement
7 Feature Engineering 20 In Agreement
8 Model Validation 19 In Agreement
9 Model Validation 22 In Agreement
10 Programming & Coding 5 In Agreement
11 Programming & Coding 7 In Agreement
12 Statistical Analysis 2 In Agreement
13 Tech Savvy 23 In Agreement
14 Theoretical Quantitative Foundations 12 In Agreement
15 Analytics Workflow 13 In Disagreement
16 Analytics Workflow 4 In Disagreement
17 Business Intelligence 18 In Disagreement
18 Business Intelligence 8 In Disagreement
19 Data Analysis & Interpretation 14 In Disagreement
20 Munging data 21 In Disagreement
21 Statistical Analysis 16 In Disagreement
22 Visualization 1 In Disagreement
23 Visualization 17 In Disagreement



Analysis of Agreement Among Raters
 
Employees in Disagreement

# Employee Agreement with Manager
1 75121 In Agreement
2 10338 In Agreement
3 10345 In Agreement
4 10347 In Agreement
5 10348 In Agreement
6 10349 In Agreement
7 10350 In Agreement
8 21007 In Agreement
9 21009 In Agreement
10 21010 In Agreement
11 21011 In Agreement
12 21014 In Agreement
13 21015 In Agreement
14 21016 In Agreement
15 21019 In Agreement
16 22867 In Agreement
17 35903 In Agreement
18 75122 In Disagreement
19 10335 In Disagreement
20 10336 In Disagreement
21 10337 In Disagreement
22 10343 In Disagreement
23 10344 In Disagreement
24 21008 In Disagreement
25 21012 In Disagreement
26 21013 In Disagreement
27 21017 In Disagreement
28 21018 In Disagreement
29 21020 In Disagreement
30 22868 In Disagreement
31 23595 In Disagreement
32 26674 In Disagreement
33 26675 In Disagreement



Abilities of Respondents and Difficulties of Items
Employees and Items in disagreement are excluded
 
Abilities of Employees
 
Abilities of the employees are estimated using Polytomous Rasch model applied to survey responses.
Lower numbers mean lower abilities, higher numbers mean higher abilities.

# Employee Ability OutFit
1 10349 -0.154 1.294
2 21007 0.000 0.751
3 21016 0.305 1.404
4 21009 0.455 0.381
5 21015 0.726 0.702
6 10345 0.755 1.265
7 22867 0.755 2.332
8 21011 1.211 0.749
9 21010 1.367 1.031
10 21014 1.417 0.446
11 35903 1.515 1.258
12 75121 2.558 0.898
13 10348 2.751 1.151
14 21019 2.751 0.276
15 10338 3.161 0.328
16 10347 3.161 1.871
17 10350 4.752 0.533

Note: An OutFit value greater than 1.3 indicates an outlier.



Plot of new_abil by obs



Abilities of Respondents and Difficulties of Items
 
Difficulties of Items
 
Difficulties of the survey items are estimated using Polytomous Rasch model applied to survey responses.
 
Difficulties of items identify strengths and gaps of the team:
- items with the lowest difficulties relate to team strengths;
- items with the higher difficulties relate to team gaps.

# Item Difficulty OutFit Status
1 Theoretical Quant Foundations 12 -1.917 1.472 Team Strength
2 Programming & Coding 5 -0.945 0.696 Team Strength
3 Statistical Analysis 2 -0.802 0.673  
4 Data Analysis & Interpretation 15 -0.802 0.642  
5 Model Validation 22 -0.639 1.301  
6 Tech Savvy 23 -0.392 1.717  
7 Data Gap Identification 3 -0.319 0.986  
8 Model Validation 19 0.442 0.911 Team Gap
9 Feature Engineering 10 0.619 0.658 Team Gap
10 Programming & Coding 7 0.862 2.082 Team Gap
11 Data Monetization 9 0.882 0.285 Team Gap
12 Feature Engineering 20 0.985 0.666 Team Gap
13 Data Monetization 6 1.013 0.753 Team Gap
14 Business Intelligence 11 1.013 1.037 Team Gap

Note: An OutFit value greater than 1.3 indicates an outlier.



Plot of diff_adj by item_label



Plot of prob1_1 by ability



Plot of prob2_1 by ability



Plot of prob3_1 by ability



Plot of prob4_1 by ability



Plot of prob5_1 by ability



Plot of prob6_1 by ability



Plot of prob7_1 by ability



Plot of prob8_1 by ability



Plot of prob9_1 by ability



Plot of prob10_1 by ability



Plot of prob11_1 by ability



Plot of prob12_1 by ability



Plot of prob13_1 by ability



Plot of prob14_1 by ability



Success Profile: Technical Capabilities
 
 
Relational Bayesian Networks were used to identify foundational items.
Graphs located here.

# Item Difficulty Category Item Importance
1 Theoretical Quant Foundations 12 -1.917 5  
2 Programming & Coding 5 -0.945 5  
3 Data Analysis & Interpretation 15 -0.802 5 Foundational
4 Statistical Analysis 2 -0.802 5 Foundational
5 Model Validation 22 -0.639 5  
6 Tech Savvy 23 -0.392 4  
7 Data Gap Identification 3 -0.319 4 Foundational
8 Model Validation 19 0.442 4  
9 Feature Engineering 10 0.619 4  
10 Programming & Coding 7 0.862 4 Foundational
11 Data Monetization 9 0.882 4  
12 Feature Engineering 20 0.985 4  
13 Business Intelligence 11 1.013 4  
14 Data Monetization 6 1.013 4  



Relationships Among Items
 
Relational Bayesian Networks identify relations among different items.
The results of this analysis show what foundational knowledge should be
improved in order to improve other technical characteristics measured by the survey items.
To view Relational Bayesian Networks graphs, click on a Impacted Item in the table below.

Foundational Items Impacted Items
Data Analysis & Interpretation 15 Model Validation 19
  Feature Engineering 20
  Model Validation 22
  Ability
  Data Gap Identification 3
  Data Monetization 6
  Data Monetization 9
  Feature Engineering 10
  Theoretical Quant Foundations 12
Data Gap Identification 3 Statistical Analysis 2
  Data Analysis & Interpretation 15
  Model Validation 19
  Feature Engineering 20
  Model Validation 22
  Tech Savvy 23
  Ability
  Programming & Coding 5
  Data Monetization 6
  Programming & Coding 7
  Data Monetization 9
  Feature Engineering 10
  Business Intelligence 11
  Theoretical Quant Foundations 12
Data Monetization 6 Data Analysis & Interpretation 15
  Model Validation 19
  Tech Savvy 23
  Programming & Coding 7
  Feature Engineering 10
  Business Intelligence 11
Feature Engineering 10 Statistical Analysis 2
  Tech Savvy 23
  Programming & Coding 5
  Programming & Coding 7
  Business Intelligence 11
Programming & Coding 7 Statistical Analysis 2
  Data Analysis & Interpretation 15
  Model Validation 19
  Feature Engineering 20
  Model Validation 22
  Tech Savvy 23
  Ability
  Data Gap Identification 3
  Programming & Coding 5
  Data Monetization 6
  Data Monetization 9
  Feature Engineering 10
  Business Intelligence 11
  Theoretical Quant Foundations 12
Statistical Analysis 2 Feature Engineering 20
  Model Validation 22
  Ability
  Data Gap Identification 3
  Data Monetization 6
  Data Monetization 9
  Theoretical Quant Foundations 12
Tech Savvy 23 Statistical Analysis 2
  Data Analysis & Interpretation 15
  Data Gap Identification 3
  Programming & Coding 5
  Programming & Coding 7



The SGPlot Procedure



Pie chart of fit



Pie chart of Region



Pie chart of Country



Pie chart of Highest_level_of_eduction



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 10350
Ability 4.752

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 4 0.000 0.000 0.028 0.441 0.531 5 4 Fit
Data Analysis & Interpretation 15 5 0.000 0.000 0.001 0.119 0.880 5 5 Fit
Data Gap Identification 3 5 0.000 0.000 0.003 0.179 0.818 5 4 Strength
Data Monetization 6 4 0.000 0.000 0.028 0.441 0.531 5 4 Fit
Data Monetization 9 4 0.000 0.000 0.023 0.412 0.565 5 4 Fit
Feature Engineering 10 5 0.000 0.000 0.015 0.354 0.631 5 4 Strength
Feature Engineering 20 5 0.000 0.000 0.027 0.435 0.538 5 4 Strength
Model Validation 19 4 0.000 0.000 0.011 0.316 0.673 5 4 Fit
Model Validation 22 5 0.000 0.000 0.002 0.137 0.861 5 5 Fit
Programming & Coding 5 5 0.000 0.000 0.001 0.105 0.894 5 5 Fit
Programming & Coding 7 5 0.000 0.000 0.022 0.407 0.570 5 4 Strength
Statistical Analysis 2 5 0.000 0.000 0.001 0.119 0.880 5 5 Fit
Tech Savvy 23 5 0.000 0.000 0.003 0.169 0.828 5 4 Strength
Theoretical Quant Foundations 12 5 0.000 0.000 0.000 0.042 0.957 5 5 Fit



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 10338
Ability 3.161

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 4 0.000 0.012 0.196 0.636 0.156 4 4 Fit
Data Analysis & Interpretation 15 5 0.000 0.000 0.020 0.391 0.589 5 5 Fit
Data Gap Identification 3 4 0.000 0.001 0.040 0.497 0.462 4 4 Fit
Data Monetization 6 4 0.000 0.012 0.196 0.636 0.156 4 4 Fit
Data Monetization 9 4 0.000 0.010 0.172 0.639 0.179 4 4 Fit
Feature Engineering 10 4 0.000 0.006 0.131 0.633 0.230 4 4 Fit
Feature Engineering 20 4 0.000 0.012 0.191 0.637 0.161 4 4 Fit
Model Validation 19 4 0.000 0.004 0.108 0.619 0.269 4 4 Fit
Model Validation 22 5 0.000 0.000 0.025 0.427 0.547 5 5 Fit
Programming & Coding 5 5 0.000 0.000 0.016 0.359 0.625 5 5 Fit
Programming & Coding 7 4 0.000 0.009 0.169 0.639 0.183 4 4 Fit
Statistical Analysis 2 4 0.000 0.000 0.020 0.391 0.589 5 5 Opportunity
Tech Savvy 23 4 0.000 0.001 0.036 0.482 0.481 4 4 Fit
Theoretical Quant Foundations 12 5 0.000 0.000 0.003 0.178 0.819 5 5 Fit



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 10347
Ability 3.161

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 5 0.000 0.012 0.196 0.636 0.156 4 4 Fit
Data Analysis & Interpretation 15 5 0.000 0.000 0.020 0.391 0.589 5 5 Fit
Data Gap Identification 3 5 0.000 0.001 0.040 0.497 0.462 4 4 Fit
Data Monetization 6 4 0.000 0.012 0.196 0.636 0.156 4 4 Fit
Data Monetization 9 4 0.000 0.010 0.172 0.639 0.179 4 4 Fit
Feature Engineering 10 4 0.000 0.006 0.131 0.633 0.230 4 4 Fit
Feature Engineering 20 4 0.000 0.012 0.191 0.637 0.161 4 4 Fit
Model Validation 19 5 0.000 0.004 0.108 0.619 0.269 4 4 Fit
Model Validation 22 5 0.000 0.000 0.025 0.427 0.547 5 5 Fit
Programming & Coding 5 4 0.000 0.000 0.016 0.359 0.625 5 5 Opportunity
Programming & Coding 7 2 0.000 0.009 0.169 0.639 0.183 4 4 Opportunity
Statistical Analysis 2 5 0.000 0.000 0.020 0.391 0.589 5 5 Fit
Tech Savvy 23 3 0.000 0.001 0.036 0.482 0.481 4 4 Opportunity
Theoretical Quant Foundations 12 5 0.000 0.000 0.003 0.178 0.819 5 5 Fit



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 10348
Ability 2.751

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 3 0.000 0.026 0.277 0.599 0.097 4 4 Opportunity
Data Analysis & Interpretation 15 5 0.000 0.001 0.036 0.482 0.481 4 5 Fit
Data Gap Identification 3 4 0.000 0.002 0.070 0.574 0.354 4 4 Fit
Data Monetization 6 3 0.000 0.026 0.277 0.599 0.097 4 4 Opportunity
Data Monetization 9 4 0.000 0.021 0.250 0.615 0.114 4 4 Fit
Feature Engineering 10 3 0.000 0.013 0.199 0.635 0.153 4 4 Opportunity
Feature Engineering 20 4 0.000 0.025 0.271 0.602 0.101 4 4 Fit
Model Validation 19 3 0.000 0.009 0.167 0.639 0.184 4 4 Opportunity
Model Validation 22 4 0.000 0.001 0.046 0.515 0.438 4 5 Gap
Programming & Coding 5 5 0.000 0.000 0.029 0.450 0.520 5 5 Fit
Programming & Coding 7 5 0.000 0.020 0.246 0.617 0.117 4 4 Fit
Statistical Analysis 2 5 0.000 0.001 0.036 0.482 0.481 4 5 Fit
Tech Savvy 23 5 0.000 0.001 0.064 0.562 0.373 4 4 Fit
Theoretical Quant Foundations 12 5 0.000 0.000 0.006 0.245 0.748 5 5 Fit



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 21019
Ability 2.751

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 4 0.000 0.026 0.277 0.599 0.097 4 4 Fit
Data Analysis & Interpretation 15 4 0.000 0.001 0.036 0.482 0.481 4 5 Gap
Data Gap Identification 3 4 0.000 0.002 0.070 0.574 0.354 4 4 Fit
Data Monetization 6 4 0.000 0.026 0.277 0.599 0.097 4 4 Fit
Data Monetization 9 4 0.000 0.021 0.250 0.615 0.114 4 4 Fit
Feature Engineering 10 4 0.000 0.013 0.199 0.635 0.153 4 4 Fit
Feature Engineering 20 4 0.000 0.025 0.271 0.602 0.101 4 4 Fit
Model Validation 19 4 0.000 0.009 0.167 0.639 0.184 4 4 Fit
Model Validation 22 4 0.000 0.001 0.046 0.515 0.438 4 5 Gap
Programming & Coding 5 5 0.000 0.000 0.029 0.450 0.520 5 5 Fit
Programming & Coding 7 4 0.000 0.020 0.246 0.617 0.117 4 4 Fit
Statistical Analysis 2 4 0.000 0.001 0.036 0.482 0.481 4 5 Gap
Tech Savvy 23 4 0.000 0.001 0.064 0.562 0.373 4 4 Fit
Theoretical Quant Foundations 12 5 0.000 0.000 0.006 0.245 0.748 5 5 Fit



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 75121
Ability 2.558

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 4 0.000 0.037 0.318 0.568 0.077 4 4 Fit
Data Analysis & Interpretation 15 4 0.000 0.001 0.048 0.521 0.431 4 5 Gap
Data Gap Identification 3 4 0.000 0.003 0.089 0.601 0.307 4 4 Fit
Data Monetization 6 4 0.000 0.037 0.318 0.568 0.077 4 4 Fit
Data Monetization 9 4 0.000 0.029 0.290 0.590 0.091 4 4 Fit
Feature Engineering 10 3 0.000 0.018 0.235 0.622 0.124 4 4 Opportunity
Feature Engineering 20 4 0.000 0.035 0.312 0.573 0.079 4 4 Fit
Model Validation 19 4 0.000 0.013 0.201 0.635 0.151 4 4 Fit
Model Validation 22 5 0.000 0.001 0.059 0.552 0.388 4 5 Fit
Programming & Coding 5 5 0.000 0.001 0.039 0.491 0.469 4 5 Fit
Programming & Coding 7 3 0.000 0.028 0.286 0.593 0.093 4 4 Opportunity
Statistical Analysis 2 5 0.000 0.001 0.048 0.521 0.431 4 5 Fit
Tech Savvy 23 3 0.000 0.002 0.081 0.592 0.325 4 4 Opportunity
Theoretical Quant Foundations 12 5 0.000 0.000 0.008 0.282 0.710 5 5 Fit



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 35903
Ability 1.515

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 3 0.002 0.164 0.502 0.317 0.015 3 4 Gap
Data Analysis & Interpretation 15 5 0.000 0.009 0.165 0.639 0.187 4 5 Fit
Data Gap Identification 3 4 0.000 0.022 0.256 0.612 0.110 4 4 Fit
Data Monetization 6 3 0.002 0.164 0.502 0.317 0.015 3 4 Gap
Data Monetization 9 3 0.001 0.140 0.488 0.351 0.019 3 4 Gap
Feature Engineering 10 3 0.001 0.099 0.450 0.421 0.030 3 4 Gap
Feature Engineering 20 2 0.002 0.159 0.500 0.324 0.016 3 4 Gap
Model Validation 19 3 0.001 0.077 0.417 0.466 0.039 4 4 Opportunity
Model Validation 22 . 0.000 0.012 0.193 0.636 0.158 4 5 Gap
Programming & Coding 5 4 0.000 0.007 0.142 0.636 0.215 4 5 Gap
Programming & Coding 7 2 0.001 0.136 0.486 0.357 0.020 3 4 Gap
Statistical Analysis 2 5 0.000 0.009 0.165 0.639 0.187 4 5 Fit
Tech Savvy 23 5 0.000 0.019 0.241 0.619 0.120 4 4 Fit
Theoretical Quant Foundations 12 5 0.000 0.001 0.043 0.506 0.451 4 5 Fit



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 21014
Ability 1.417

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 3 0.002 0.184 0.510 0.291 0.013 3 4 Gap
Data Analysis & Interpretation 15 4 0.000 0.011 0.182 0.638 0.169 4 5 Gap
Data Gap Identification 3 4 0.000 0.026 0.277 0.599 0.098 4 4 Fit
Data Monetization 6 2 0.002 0.184 0.510 0.291 0.013 3 4 Gap
Data Monetization 9 3 0.002 0.158 0.499 0.325 0.016 3 4 Gap
Feature Engineering 10 4 0.001 0.113 0.466 0.394 0.025 3 4 Fit
Feature Engineering 20 3 0.002 0.178 0.508 0.298 0.013 3 4 Gap
Model Validation 19 . 0.001 0.089 0.436 0.441 0.034 4 4 Opportunity
Model Validation 22 . 0.000 0.015 0.212 0.631 0.142 4 5 Gap
Programming & Coding 5 4 0.000 0.008 0.158 0.639 0.195 4 5 Gap
Programming & Coding 7 4 0.002 0.154 0.497 0.330 0.017 3 4 Fit
Statistical Analysis 2 4 0.000 0.011 0.182 0.638 0.169 4 5 Gap
Tech Savvy 23 4 0.000 0.023 0.262 0.608 0.107 4 4 Fit
Theoretical Quant Foundations 12 4 0.000 0.001 0.049 0.526 0.424 4 5 Gap



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 21010
Ability 1.367

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 4 0.003 0.194 0.513 0.278 0.011 3 4 Fit
Data Analysis & Interpretation 15 4 0.000 0.012 0.191 0.637 0.160 4 5 Gap
Data Gap Identification 3 4 0.000 0.029 0.288 0.592 0.092 4 4 Fit
Data Monetization 6 4 0.003 0.194 0.513 0.278 0.011 3 4 Fit
Data Monetization 9 3 0.002 0.167 0.504 0.312 0.015 3 4 Gap
Feature Engineering 10 3 0.001 0.121 0.474 0.381 0.023 3 4 Gap
Feature Engineering 20 3 0.003 0.188 0.511 0.285 0.012 3 4 Gap
Model Validation 19 4 0.001 0.095 0.445 0.428 0.031 3 4 Fit
Model Validation 22 5 0.000 0.016 0.222 0.628 0.134 4 5 Fit
Programming & Coding 5 3 0.000 0.009 0.166 0.639 0.186 4 5 Gap
Programming & Coding 7 2 0.002 0.164 0.502 0.317 0.015 3 4 Gap
Statistical Analysis 2 4 0.000 0.012 0.191 0.637 0.160 4 5 Gap
Tech Savvy 23 3 0.000 0.025 0.272 0.602 0.100 4 4 Opportunity
Theoretical Quant Foundations 12 4 0.000 0.001 0.053 0.535 0.411 4 5 Gap



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 21011
Ability 1.211

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 2 0.004 0.230 0.518 0.240 0.008 3 4 Gap
Data Analysis & Interpretation 15 4 0.000 0.016 0.221 0.628 0.135 4 5 Gap
Data Gap Identification 3 4 0.000 0.038 0.322 0.565 0.075 4 4 Fit
Data Monetization 6 3 0.004 0.230 0.518 0.240 0.008 3 4 Gap
Data Monetization 9 3 0.003 0.201 0.514 0.271 0.011 3 4 Gap
Feature Engineering 10 4 0.002 0.148 0.494 0.339 0.018 3 4 Fit
Feature Engineering 20 2 0.004 0.224 0.518 0.246 0.009 3 4 Gap
Model Validation 19 4 0.001 0.118 0.471 0.386 0.024 3 4 Fit
Model Validation 22 4 0.000 0.022 0.254 0.613 0.112 4 5 Gap
Programming & Coding 5 4 0.000 0.012 0.194 0.636 0.157 4 5 Gap
Programming & Coding 7 2 0.003 0.196 0.513 0.276 0.011 3 4 Gap
Statistical Analysis 2 4 0.000 0.016 0.221 0.628 0.135 4 5 Gap
Tech Savvy 23 4 0.000 0.033 0.307 0.578 0.082 4 4 Fit
Theoretical Quant Foundations 12 5 0.000 0.002 0.065 0.564 0.369 4 5 Fit



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 22867
Ability 0.755

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 3 0.009 0.346 0.496 0.146 0.003 3 4 Gap
Data Analysis & Interpretation 15 3 0.000 0.036 0.315 0.571 0.078 4 5 Gap
Data Gap Identification 3 4 0.001 0.077 0.417 0.466 0.039 4 4 Fit
Data Monetization 6 3 0.009 0.346 0.496 0.146 0.003 3 4 Gap
Data Monetization 9 3 0.007 0.310 0.507 0.171 0.004 3 4 Gap
Feature Engineering 10 3 0.004 0.244 0.518 0.226 0.007 3 4 Gap
Feature Engineering 20 4 0.009 0.338 0.499 0.151 0.003 3 4 Fit
Model Validation 19 2 0.003 0.203 0.515 0.269 0.011 3 4 Gap
Model Validation 22 2 0.000 0.047 0.351 0.540 0.063 4 5 Gap
Programming & Coding 5 4 0.000 0.028 0.284 0.594 0.094 4 5 Gap
Programming & Coding 7 4 0.007 0.305 0.509 0.175 0.005 3 4 Fit
Statistical Analysis 2 4 0.000 0.036 0.315 0.571 0.078 4 5 Gap
Tech Savvy 23 5 0.000 0.069 0.403 0.484 0.044 4 4 Fit
Theoretical Quant Foundations 12 2 0.000 0.004 0.113 0.623 0.260 4 5 Gap



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 10345
Ability 0.755

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 2 0.009 0.346 0.496 0.146 0.003 3 4 Gap
Data Analysis & Interpretation 15 3 0.000 0.036 0.315 0.571 0.078 4 5 Gap
Data Gap Identification 3 4 0.001 0.077 0.417 0.466 0.039 4 4 Fit
Data Monetization 6 4 0.009 0.346 0.496 0.146 0.003 3 4 Fit
Data Monetization 9 3 0.007 0.310 0.507 0.171 0.004 3 4 Gap
Feature Engineering 10 4 0.004 0.244 0.518 0.226 0.007 3 4 Fit
Feature Engineering 20 3 0.009 0.338 0.499 0.151 0.003 3 4 Gap
Model Validation 19 3 0.003 0.203 0.515 0.269 0.011 3 4 Gap
Model Validation 22 4 0.000 0.047 0.351 0.540 0.063 4 5 Gap
Programming & Coding 5 4 0.000 0.028 0.284 0.594 0.094 4 5 Gap
Programming & Coding 7 2 0.007 0.305 0.509 0.175 0.005 3 4 Gap
Statistical Analysis 2 3 0.000 0.036 0.315 0.571 0.078 4 5 Gap
Tech Savvy 23 2 0.000 0.069 0.403 0.484 0.044 4 4 Opportunity
Theoretical Quant Foundations 12 5 0.000 0.004 0.113 0.623 0.260 4 5 Fit



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 21015
Ability 0.726

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 . 0.009 0.354 0.492 0.141 0.003 3 4 Gap
Data Analysis & Interpretation 15 3 0.000 0.038 0.322 0.565 0.075 4 5 Gap
Data Gap Identification 3 . 0.001 0.080 0.423 0.459 0.038 4 4 Opportunity
Data Monetization 6 . 0.009 0.354 0.492 0.141 0.003 3 4 Gap
Data Monetization 9 . 0.007 0.318 0.505 0.165 0.004 3 4 Gap
Feature Engineering 10 . 0.005 0.251 0.518 0.220 0.007 3 4 Gap
Feature Engineering 20 3 0.009 0.346 0.496 0.146 0.003 3 4 Gap
Model Validation 19 3 0.003 0.209 0.516 0.261 0.010 3 4 Gap
Model Validation 22 3 0.000 0.049 0.357 0.533 0.060 4 5 Gap
Programming & Coding 5 4 0.000 0.029 0.291 0.589 0.090 4 5 Gap
Programming & Coding 7 4 0.007 0.313 0.507 0.169 0.004 3 4 Fit
Statistical Analysis 2 3 0.000 0.038 0.322 0.565 0.075 4 5 Gap
Tech Savvy 23 4 0.000 0.072 0.409 0.477 0.042 4 4 Fit
Theoretical Quant Foundations 12 4 0.000 0.004 0.117 0.626 0.253 4 5 Gap



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 21009
Ability 0.455

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 2 0.015 0.429 0.455 0.099 0.002 3 4 Gap
Data Analysis & Interpretation 15 4 0.000 0.058 0.380 0.510 0.052 4 5 Gap
Data Gap Identification 3 3 0.001 0.117 0.469 0.389 0.024 3 4 Gap
Data Monetization 6 2 0.015 0.429 0.455 0.099 0.002 3 4 Gap
Data Monetization 9 3 0.012 0.392 0.475 0.118 0.002 3 4 Gap
Feature Engineering 10 3 0.008 0.320 0.505 0.163 0.004 3 4 Gap
Feature Engineering 20 3 0.014 0.421 0.460 0.103 0.002 3 4 Gap
Model Validation 19 3 0.005 0.274 0.515 0.199 0.006 3 4 Gap
Model Validation 22 3 0.000 0.075 0.413 0.471 0.041 4 5 Gap
Programming & Coding 5 4 0.000 0.046 0.349 0.541 0.063 4 5 Gap
Programming & Coding 7 3 0.012 0.387 0.478 0.121 0.002 3 4 Gap
Statistical Analysis 2 3 0.000 0.058 0.380 0.510 0.052 4 5 Gap
Tech Savvy 23 4 0.001 0.106 0.458 0.408 0.027 3 4 Fit
Theoretical Quant Foundations 12 4 0.000 0.008 0.156 0.639 0.197 4 5 Gap



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 21016
Ability 0.305

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 4 0.019 0.472 0.427 0.080 0.001 2 4 Fit
Data Analysis & Interpretation 15 3 0.000 0.074 0.413 0.472 0.041 4 5 Gap
Data Gap Identification 3 4 0.002 0.143 0.491 0.346 0.018 3 4 Fit
Data Monetization 6 3 0.019 0.472 0.427 0.080 0.001 2 4 Gap
Data Monetization 9 2 0.016 0.437 0.451 0.096 0.002 3 4 Gap
Feature Engineering 10 3 0.010 0.364 0.488 0.135 0.003 3 4 Gap
Feature Engineering 20 2 0.019 0.465 0.433 0.083 0.001 2 4 Gap
Model Validation 19 3 0.007 0.316 0.506 0.167 0.004 3 4 Gap
Model Validation 22 2 0.001 0.094 0.443 0.431 0.032 3 5 Gap
Programming & Coding 5 4 0.000 0.060 0.383 0.506 0.050 4 5 Gap
Programming & Coding 7 4 0.015 0.431 0.454 0.098 0.002 3 4 Fit
Statistical Analysis 2 3 0.000 0.074 0.413 0.472 0.041 4 5 Gap
Tech Savvy 23 3 0.001 0.131 0.482 0.365 0.021 3 4 Gap
Theoretical Quant Foundations 12 3 0.000 0.011 0.183 0.638 0.168 4 5 Gap



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 21007
Ability 0.000

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 2 0.030 0.550 0.368 0.051 0.001 2 4 Gap
Data Analysis & Interpretation 15 3 0.001 0.113 0.466 0.395 0.025 3 5 Gap
Data Gap Identification 3 4 0.003 0.203 0.515 0.269 0.011 3 4 Fit
Data Monetization 6 2 0.030 0.550 0.368 0.051 0.001 2 4 Gap
Data Monetization 9 2 0.025 0.517 0.395 0.062 0.001 2 4 Gap
Feature Engineering 10 2 0.017 0.447 0.444 0.091 0.001 2 4 Gap
Feature Engineering 20 2 0.029 0.543 0.374 0.053 0.001 2 4 Gap
Model Validation 19 4 0.012 0.398 0.472 0.115 0.002 3 4 Fit
Model Validation 22 4 0.001 0.140 0.488 0.352 0.019 3 5 Gap
Programming & Coding 5 3 0.001 0.093 0.442 0.432 0.032 3 5 Gap
Programming & Coding 7 3 0.024 0.512 0.399 0.064 0.001 2 4 Gap
Statistical Analysis 2 3 0.001 0.113 0.466 0.395 0.025 3 5 Gap
Tech Savvy 23 3 0.003 0.187 0.511 0.287 0.012 3 4 Gap
Theoretical Quant Foundations 12 4 0.000 0.019 0.240 0.620 0.121 4 5 Gap



Individual Employee Strength, Fit, Opportunity and Gap
 
Employee 10349
Ability -0.154

Item Answered
Category
Prob. Answering
Category 1
Prob. Answering
Category 2
Prob. Answering
Category 3
Prob. Answering
Category 4
Prob. Answering
Category 5
Most Likely
Category
Success
Profile
Status
Business Intelligence 11 2 0.038 0.585 0.337 0.040 0.000 2 4 Gap
Data Analysis & Interpretation 15 4 0.001 0.137 0.487 0.355 0.020 3 5 Gap
Data Gap Identification 3 2 0.004 0.237 0.518 0.233 0.008 3 4 Gap
Data Monetization 6 2 0.038 0.585 0.337 0.040 0.000 2 4 Gap
Data Monetization 9 3 0.031 0.554 0.365 0.049 0.001 2 4 Gap
Feature Engineering 10 2 0.021 0.487 0.417 0.073 0.001 2 4 Gap
Feature Engineering 20 2 0.036 0.578 0.343 0.042 0.000 2 4 Gap
Model Validation 19 2 0.016 0.439 0.449 0.094 0.001 3 4 Gap
Model Validation 22 4 0.002 0.167 0.504 0.312 0.015 3 5 Gap
Programming & Coding 5 2 0.001 0.114 0.467 0.393 0.025 3 5 Gap
Programming & Coding 7 2 0.030 0.549 0.369 0.051 0.001 2 4 Gap
Statistical Analysis 2 4 0.001 0.137 0.487 0.355 0.020 3 5 Gap
Tech Savvy 23 4 0.003 0.220 0.517 0.250 0.009 3 4 Fit
Theoretical Quant Foundations 12 5 0.000 0.025 0.271 0.603 0.101 4 5 Fit