Exousia

Success Profile for Data Scientist Role

Snapshot of the data used for analysis

Survey Items	Self-Rated Emp Id 75121	Manager Rated Emp Id 75121	Self-Rated Emp Id 75122	Manager Rated Emp Id 75122	Self-Rated Emp Id 10335	Manager Rated Emp Id 10335
Visualization	5	5	4	4	5	5
Statistical Analysis	5	5	4	5	4	4
Data Gap Identification	5	4	4	4	5	4
Analytics Workflow	5	5	5	5	4	4
Programming & Coding Advanced	5	5	4	5	4	5
Data Monetization	4	4	3	4	4	5
Programming & Coding	2	3	4	5	3	4
Business Intelligence	5	4	2	3	4	4
Data Monetization Applications	5	4	3	4	4	4
Feature Engineering	4	3	4	4	5	4
Business Intelligence Applications	4	4	2	4	4	5
Theoretical Quant Foundations	4	5	5	4	4	4
Analytics Workflow Advanced	5	4	3	5	5	4
Data Analysis & Interpretation	5	4	4	5	5	4
Data Analysis	4	4	4	5	4	4
Statistical Analysis Advanced	3	2	4	5	5	4
Visualization Advanced	5	3	2	4	4	4
Business Intelligence Advanced	5	3	2	5	4	4
Model Interpretation	3	4	3	.	4	3
Feature Engineering Advanced	4	4	3	4	4	4
Munging data	5	4	4	5	4	5
Model Validation	5	5	4	4	4	4
Tech Savvy	.	3	4	4	4	4

Analysis of the Consistency in Ratings

Agreement on Items Among Employees and Managers

Survey Items	Agreement/Disagreement
Visualization	In Disagreement
Statistical Analysis	In Agreement
Data Gap Identification	In Agreement
Analytics Workflow	In Disagreement
Programming & Coding Advanced	In Agreement
Data Monetization	In Agreement
Programming & Coding	In Agreement
Business Intelligence	In Disagreement
Data Monetization Applications	In Agreement
Feature Engineering	In Agreement
Business Intelligence Applications	In Agreement
Theoretical Quant Foundations	In Agreement
Analytics Workflow Advanced	In Disagreement
Data Analysis & Interpretation	In Disagreement
Data Analysis	In Agreement
Statistical Analysis Advanced	In Disagreement
Visualization Advanced	In Disagreement
Business Intelligence Advanced	In Disagreement
Model Interpretation	In Agreement
Feature Engineering Advanced	In Agreement
Munging data	In Disagreement
Model Validation	In Agreement
Tech Savvy	In Agreement

Analysis of the Consistency in Ratings

Agreement Between Self-Rating and Manager Rating per Employee

Emp Id	Agreement/Disagreement
75121	In Agreement
10338	In Agreement
10345	In Agreement
10347	In Agreement
10348	In Agreement
10349	In Agreement
10350	In Agreement
21007	In Agreement
21009	In Agreement
21010	In Agreement
21011	In Agreement
21014	In Agreement
21015	In Agreement
21016	In Agreement
21019	In Agreement
22867	In Agreement
35903	In Agreement
75122	In Disagreement
10335	In Disagreement
10336	In Disagreement
10337	In Disagreement
10343	In Disagreement
10344	In Disagreement
21008	In Disagreement
21012	In Disagreement
21013	In Disagreement
21017	In Disagreement
21018	In Disagreement
21020	In Disagreement
22868	In Disagreement
23595	In Disagreement
26674	In Disagreement
26675	In Disagreement

Ability of Respondents and Difficulty of Items

(Employees for whom self-rating is in disagreement with manager rating are excluded from the analysis.

Items rated in disagreement by employees and managers are excluded from the analysis.)

Ability of Employees

Ability of the employees are estimated using Polytomous Rasch model applied to performance review survey data.

Lower numbers mean lower ability, higher numbers mean higher ability.

Employee	Ability	OutFit
10349	-0.154	1.294
21007	0.000	0.751
21016	0.305	1.404
21009	0.455	0.381
21015	0.726	0.702
10345	0.755	1.265
22867	0.755	2.332
21011	1.211	0.749
21010	1.367	1.031
21014	1.417	0.446
35903	1.515	1.258
75121	2.558	0.898
10348	2.751	1.151
21019	2.751	0.276
10338	3.161	0.328
10347	3.161	1.871
10350	4.752	0.533

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

Ability of Respondents and Difficulty of Items

(Employees for whom self-rating is in disagreement with manager rating are excluded from the analysis.

Items rated in disagreement by employees and managers are excluded from the analysis.)

Difficulty of Items

Difficulty of the survey items are estimated using Polytomous Rasch model applied to performance review survey data.

Items with the lowest difficulty relate to team strengths, items with the higher difficulty relate to team weaknesses.

Item	Difficulty	OutFit	Status
Theoretical Quant Foundations	-1.917	1.472	Team Strength
Programming & Coding Advanced	-0.945	0.696	Team Strength
Statistical Analysis	-0.802	0.673
Data Analysis	-0.802	0.642
Model Validation	-0.639	1.301
Tech Savvy	-0.392	1.717
Data Gap Identification	-0.319	0.986
Model Interpretation	0.442	0.911	Team Weakness
Feature Engineering	0.619	0.658	Team Weakness
Programming & Coding	0.862	2.082	Team Weakness
Data Monetization Applications	0.882	0.285	Team Weakness
Feature Engineering Advanced	0.985	0.666	Team Weakness
Data Monetization	1.013	0.753	Team Weakness
Business Intelligence Applications	1.013	1.037	Team Weakness

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

Success Profile for Data Scientist Role

Relational Bayesian Networks were used to identify foundational items

Graphs located here.

Item	Difficulty	Score	Item Importance
Theoretical Quant Foundations	-1.917	5
Programming & Coding Advanced	-0.945	5
Data Analysis	-0.802	5	Foundational
Statistical Analysis	-0.802	5	Foundational
Model Validation	-0.639	5
Tech Savvy	-0.392	4
Data Gap Identification	-0.319	4	Foundational
Model Interpretation	0.442	4
Feature Engineering	0.619	4
Programming & Coding	0.862	4	Foundational
Data Monetization Applications	0.882	4
Feature Engineering Advanced	0.985	4
Business Intelligence Applications	1.013	4
Data Monetization	1.013	4

Relationships Among Items

Relational Bayesian Networks identify causal relationships among different items.

The results of this analysis show what foundational knowledge should be improved

in order to boost other technical characteristics measured by the performance review.

To view Relational Bayesian Networks graphs, click on an Impacted Item in the table below.

Foundational Items	Impacted Items
Data Analysis	Model Interpretation
	Feature Engineering Advanced
	Model Validation
	Ability
	Data Gap Identification
	Data Monetization
	Data Monetization Applications
	Feature Engineering
	Theoretical Quant Foundations
Data Gap Identification	Statistical Analysis
	Data Analysis
	Model Interpretation
	Feature Engineering Advanced
	Model Validation
	Tech Savvy
	Ability
	Programming & Coding Advanced
	Data Monetization
	Programming & Coding
	Data Monetization Applications
	Feature Engineering
	Business Intelligence Applications
	Theoretical Quant Foundations
Data Monetization	Data Analysis
	Model Interpretation
	Tech Savvy
	Programming & Coding
	Feature Engineering
	Business Intelligence Applications
Feature Engineering	Statistical Analysis
	Tech Savvy
	Programming & Coding Advanced
	Programming & Coding
	Business Intelligence Applications
Programming & Coding	Statistical Analysis
	Data Analysis
	Model Interpretation
	Feature Engineering Advanced
	Model Validation
	Tech Savvy
	Ability
	Data Gap Identification
	Programming & Coding Advanced
	Data Monetization
	Data Monetization Applications
	Feature Engineering
	Business Intelligence Applications
	Theoretical Quant Foundations
Statistical Analysis	Feature Engineering Advanced
	Model Validation
	Ability
	Data Gap Identification
	Data Monetization
	Data Monetization Applications
	Theoretical Quant Foundations
Tech Savvy	Statistical Analysis
	Data Analysis
	Data Gap Identification
	Programming & Coding Advanced
	Programming & Coding

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 10350

Ability 4.752

Item	Selected Score	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	4	0.028	0.441	0.531	5	4	Fit
Data Analysis	5	0.001	0.119	0.880	5	5	Fit
Data Gap Identification	5	0.003	0.179	0.818	5	4	Strength
Data Monetization	4	0.028	0.441	0.531	5	4	Fit
Data Monetization Applications	4	0.023	0.412	0.565	5	4	Fit
Feature Engineering	5	0.015	0.354	0.631	5	4	Strength
Feature Engineering Advanced	5	0.027	0.435	0.538	5	4	Strength
Model Interpretation	4	0.011	0.316	0.673	5	4	Fit
Model Validation	5	0.002	0.137	0.861	5	5	Fit
Programming & Coding	5	0.022	0.407	0.570	5	4	Strength
Programming & Coding Advanced	5	0.001	0.105	0.894	5	5	Fit
Statistical Analysis	5	0.001	0.119	0.880	5	5	Fit
Tech Savvy	5	0.003	0.169	0.828	5	4	Strength
Theoretical Quant Foundations	5	0.000	0.042	0.957	5	5	Fit

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 10338

Ability 3.161

Item	Selected Score	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	4	0.012	0.196	0.636	0.156	4	4	Fit
Data Analysis	5	0.000	0.020	0.391	0.589	5	5	Fit
Data Gap Identification	4	0.001	0.040	0.497	0.462	4	4	Fit
Data Monetization	4	0.012	0.196	0.636	0.156	4	4	Fit
Data Monetization Applications	4	0.010	0.172	0.639	0.179	4	4	Fit
Feature Engineering	4	0.006	0.131	0.633	0.230	4	4	Fit
Feature Engineering Advanced	4	0.012	0.191	0.637	0.161	4	4	Fit
Model Interpretation	4	0.004	0.108	0.619	0.269	4	4	Fit
Model Validation	5	0.000	0.025	0.427	0.547	5	5	Fit
Programming & Coding	4	0.009	0.169	0.639	0.183	4	4	Fit
Programming & Coding Advanced	5	0.000	0.016	0.359	0.625	5	5	Fit
Statistical Analysis	4	0.000	0.020	0.391	0.589	5	5	Opportunity
Tech Savvy	4	0.001	0.036	0.482	0.481	4	4	Fit
Theoretical Quant Foundations	5	0.000	0.003	0.178	0.819	5	5	Fit

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 10347

Ability 3.161

Item	Selected Score	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	5	0.012	0.196	0.636	0.156	4	4	Fit
Data Analysis	5	0.000	0.020	0.391	0.589	5	5	Fit
Data Gap Identification	5	0.001	0.040	0.497	0.462	4	4	Fit
Data Monetization	4	0.012	0.196	0.636	0.156	4	4	Fit
Data Monetization Applications	4	0.010	0.172	0.639	0.179	4	4	Fit
Feature Engineering	4	0.006	0.131	0.633	0.230	4	4	Fit
Feature Engineering Advanced	4	0.012	0.191	0.637	0.161	4	4	Fit
Model Interpretation	5	0.004	0.108	0.619	0.269	4	4	Fit
Model Validation	5	0.000	0.025	0.427	0.547	5	5	Fit
Programming & Coding	2	0.009	0.169	0.639	0.183	4	4	Opportunity
Programming & Coding Advanced	4	0.000	0.016	0.359	0.625	5	5	Opportunity
Statistical Analysis	5	0.000	0.020	0.391	0.589	5	5	Fit
Tech Savvy	3	0.001	0.036	0.482	0.481	4	4	Opportunity
Theoretical Quant Foundations	5	0.000	0.003	0.178	0.819	5	5	Fit

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 10348

Ability 2.751

Item	Selected Score	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	3	0.026	0.277	0.599	0.097	4	4	Opportunity
Data Analysis	5	0.001	0.036	0.482	0.481	4	5	Fit
Data Gap Identification	4	0.002	0.070	0.574	0.354	4	4	Fit
Data Monetization	3	0.026	0.277	0.599	0.097	4	4	Opportunity
Data Monetization Applications	4	0.021	0.250	0.615	0.114	4	4	Fit
Feature Engineering	3	0.013	0.199	0.635	0.153	4	4	Opportunity
Feature Engineering Advanced	4	0.025	0.271	0.602	0.101	4	4	Fit
Model Interpretation	3	0.009	0.167	0.639	0.184	4	4	Opportunity
Model Validation	4	0.001	0.046	0.515	0.438	4	5	Weakness
Programming & Coding	5	0.020	0.246	0.617	0.117	4	4	Fit
Programming & Coding Advanced	5	0.000	0.029	0.450	0.520	5	5	Fit
Statistical Analysis	5	0.001	0.036	0.482	0.481	4	5	Fit
Tech Savvy	5	0.001	0.064	0.562	0.373	4	4	Fit
Theoretical Quant Foundations	5	0.000	0.006	0.245	0.748	5	5	Fit

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 21019

Ability 2.751

Item	Selected Score	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	4	0.026	0.277	0.599	0.097	4	4	Fit
Data Analysis	4	0.001	0.036	0.482	0.481	4	5	Weakness
Data Gap Identification	4	0.002	0.070	0.574	0.354	4	4	Fit
Data Monetization	4	0.026	0.277	0.599	0.097	4	4	Fit
Data Monetization Applications	4	0.021	0.250	0.615	0.114	4	4	Fit
Feature Engineering	4	0.013	0.199	0.635	0.153	4	4	Fit
Feature Engineering Advanced	4	0.025	0.271	0.602	0.101	4	4	Fit
Model Interpretation	4	0.009	0.167	0.639	0.184	4	4	Fit
Model Validation	4	0.001	0.046	0.515	0.438	4	5	Weakness
Programming & Coding	4	0.020	0.246	0.617	0.117	4	4	Fit
Programming & Coding Advanced	5	0.000	0.029	0.450	0.520	5	5	Fit
Statistical Analysis	4	0.001	0.036	0.482	0.481	4	5	Weakness
Tech Savvy	4	0.001	0.064	0.562	0.373	4	4	Fit
Theoretical Quant Foundations	5	0.000	0.006	0.245	0.748	5	5	Fit

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 75121

Ability 2.558

Item	Selected Score	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	4	0.037	0.318	0.568	0.077	4	4	Fit
Data Analysis	4	0.001	0.048	0.521	0.431	4	5	Weakness
Data Gap Identification	4	0.003	0.089	0.601	0.307	4	4	Fit
Data Monetization	4	0.037	0.318	0.568	0.077	4	4	Fit
Data Monetization Applications	4	0.029	0.290	0.590	0.091	4	4	Fit
Feature Engineering	3	0.018	0.235	0.622	0.124	4	4	Opportunity
Feature Engineering Advanced	4	0.035	0.312	0.573	0.079	4	4	Fit
Model Interpretation	4	0.013	0.201	0.635	0.151	4	4	Fit
Model Validation	5	0.001	0.059	0.552	0.388	4	5	Fit
Programming & Coding	3	0.028	0.286	0.593	0.093	4	4	Opportunity
Programming & Coding Advanced	5	0.001	0.039	0.491	0.469	4	5	Fit
Statistical Analysis	5	0.001	0.048	0.521	0.431	4	5	Fit
Tech Savvy	3	0.002	0.081	0.592	0.325	4	4	Opportunity
Theoretical Quant Foundations	5	0.000	0.008	0.282	0.710	5	5	Fit

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 35903

Ability 1.515

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	3	0.002	0.164	0.502	0.317	0.015	3	4	Weakness
Data Analysis	5	0.000	0.009	0.165	0.639	0.187	4	5	Fit
Data Gap Identification	4	0.000	0.022	0.256	0.612	0.110	4	4	Fit
Data Monetization	3	0.002	0.164	0.502	0.317	0.015	3	4	Weakness
Data Monetization Applications	3	0.001	0.140	0.488	0.351	0.019	3	4	Weakness
Feature Engineering	3	0.001	0.099	0.450	0.421	0.030	3	4	Weakness
Feature Engineering Advanced	2	0.002	0.159	0.500	0.324	0.016	3	4	Weakness
Model Interpretation	3	0.001	0.077	0.417	0.466	0.039	4	4	Opportunity
Model Validation	.	0.000	0.012	0.193	0.636	0.158	4	5	Weakness
Programming & Coding	2	0.001	0.136	0.486	0.357	0.020	3	4	Weakness
Programming & Coding Advanced	4	0.000	0.007	0.142	0.636	0.215	4	5	Weakness
Statistical Analysis	5	0.000	0.009	0.165	0.639	0.187	4	5	Fit
Tech Savvy	5	0.000	0.019	0.241	0.619	0.120	4	4	Fit
Theoretical Quant Foundations	5	0.000	0.001	0.043	0.506	0.451	4	5	Fit

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 21014

Ability 1.417

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	3	0.002	0.184	0.510	0.291	0.013	3	4	Weakness
Data Analysis	4	0.000	0.011	0.182	0.638	0.169	4	5	Weakness
Data Gap Identification	4	0.000	0.026	0.277	0.599	0.098	4	4	Fit
Data Monetization	2	0.002	0.184	0.510	0.291	0.013	3	4	Weakness
Data Monetization Applications	3	0.002	0.158	0.499	0.325	0.016	3	4	Weakness
Feature Engineering	4	0.001	0.113	0.466	0.394	0.025	3	4	Fit
Feature Engineering Advanced	3	0.002	0.178	0.508	0.298	0.013	3	4	Weakness
Model Interpretation	.	0.001	0.089	0.436	0.441	0.034	4	4	Opportunity
Model Validation	.	0.000	0.015	0.212	0.631	0.142	4	5	Weakness
Programming & Coding	4	0.002	0.154	0.497	0.330	0.017	3	4	Fit
Programming & Coding Advanced	4	0.000	0.008	0.158	0.639	0.195	4	5	Weakness
Statistical Analysis	4	0.000	0.011	0.182	0.638	0.169	4	5	Weakness
Tech Savvy	4	0.000	0.023	0.262	0.608	0.107	4	4	Fit
Theoretical Quant Foundations	4	0.000	0.001	0.049	0.526	0.424	4	5	Weakness

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 21010

Ability 1.367

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	4	0.003	0.194	0.513	0.278	0.011	3	4	Fit
Data Analysis	4	0.000	0.012	0.191	0.637	0.160	4	5	Weakness
Data Gap Identification	4	0.000	0.029	0.288	0.592	0.092	4	4	Fit
Data Monetization	4	0.003	0.194	0.513	0.278	0.011	3	4	Fit
Data Monetization Applications	3	0.002	0.167	0.504	0.312	0.015	3	4	Weakness
Feature Engineering	3	0.001	0.121	0.474	0.381	0.023	3	4	Weakness
Feature Engineering Advanced	3	0.003	0.188	0.511	0.285	0.012	3	4	Weakness
Model Interpretation	4	0.001	0.095	0.445	0.428	0.031	3	4	Fit
Model Validation	5	0.000	0.016	0.222	0.628	0.134	4	5	Fit
Programming & Coding	2	0.002	0.164	0.502	0.317	0.015	3	4	Weakness
Programming & Coding Advanced	3	0.000	0.009	0.166	0.639	0.186	4	5	Weakness
Statistical Analysis	4	0.000	0.012	0.191	0.637	0.160	4	5	Weakness
Tech Savvy	3	0.000	0.025	0.272	0.602	0.100	4	4	Opportunity
Theoretical Quant Foundations	4	0.000	0.001	0.053	0.535	0.411	4	5	Weakness

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 21011

Ability 1.211

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	2	0.004	0.230	0.518	0.240	0.008	3	4	Weakness
Data Analysis	4	0.000	0.016	0.221	0.628	0.135	4	5	Weakness
Data Gap Identification	4	0.000	0.038	0.322	0.565	0.075	4	4	Fit
Data Monetization	3	0.004	0.230	0.518	0.240	0.008	3	4	Weakness
Data Monetization Applications	3	0.003	0.201	0.514	0.271	0.011	3	4	Weakness
Feature Engineering	4	0.002	0.148	0.494	0.339	0.018	3	4	Fit
Feature Engineering Advanced	2	0.004	0.224	0.518	0.246	0.009	3	4	Weakness
Model Interpretation	4	0.001	0.118	0.471	0.386	0.024	3	4	Fit
Model Validation	4	0.000	0.022	0.254	0.613	0.112	4	5	Weakness
Programming & Coding	2	0.003	0.196	0.513	0.276	0.011	3	4	Weakness
Programming & Coding Advanced	4	0.000	0.012	0.194	0.636	0.157	4	5	Weakness
Statistical Analysis	4	0.000	0.016	0.221	0.628	0.135	4	5	Weakness
Tech Savvy	4	0.000	0.033	0.307	0.578	0.082	4	4	Fit
Theoretical Quant Foundations	5	0.000	0.002	0.065	0.564	0.369	4	5	Fit

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 22867

Ability 0.755

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	3	0.009	0.346	0.496	0.146	0.003	3	4	Weakness
Data Analysis	3	0.000	0.036	0.315	0.571	0.078	4	5	Weakness
Data Gap Identification	4	0.001	0.077	0.417	0.466	0.039	4	4	Fit
Data Monetization	3	0.009	0.346	0.496	0.146	0.003	3	4	Weakness
Data Monetization Applications	3	0.007	0.310	0.507	0.171	0.004	3	4	Weakness
Feature Engineering	3	0.004	0.244	0.518	0.226	0.007	3	4	Weakness
Feature Engineering Advanced	4	0.009	0.338	0.499	0.151	0.003	3	4	Fit
Model Interpretation	2	0.003	0.203	0.515	0.269	0.011	3	4	Weakness
Model Validation	2	0.000	0.047	0.351	0.540	0.063	4	5	Weakness
Programming & Coding	4	0.007	0.305	0.509	0.175	0.005	3	4	Fit
Programming & Coding Advanced	4	0.000	0.028	0.284	0.594	0.094	4	5	Weakness
Statistical Analysis	4	0.000	0.036	0.315	0.571	0.078	4	5	Weakness
Tech Savvy	5	0.000	0.069	0.403	0.484	0.044	4	4	Fit
Theoretical Quant Foundations	2	0.000	0.004	0.113	0.623	0.260	4	5	Weakness

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 10345

Ability 0.755

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	2	0.009	0.346	0.496	0.146	0.003	3	4	Weakness
Data Analysis	3	0.000	0.036	0.315	0.571	0.078	4	5	Weakness
Data Gap Identification	4	0.001	0.077	0.417	0.466	0.039	4	4	Fit
Data Monetization	4	0.009	0.346	0.496	0.146	0.003	3	4	Fit
Data Monetization Applications	3	0.007	0.310	0.507	0.171	0.004	3	4	Weakness
Feature Engineering	4	0.004	0.244	0.518	0.226	0.007	3	4	Fit
Feature Engineering Advanced	3	0.009	0.338	0.499	0.151	0.003	3	4	Weakness
Model Interpretation	3	0.003	0.203	0.515	0.269	0.011	3	4	Weakness
Model Validation	4	0.000	0.047	0.351	0.540	0.063	4	5	Weakness
Programming & Coding	2	0.007	0.305	0.509	0.175	0.005	3	4	Weakness
Programming & Coding Advanced	4	0.000	0.028	0.284	0.594	0.094	4	5	Weakness
Statistical Analysis	3	0.000	0.036	0.315	0.571	0.078	4	5	Weakness
Tech Savvy	2	0.000	0.069	0.403	0.484	0.044	4	4	Opportunity
Theoretical Quant Foundations	5	0.000	0.004	0.113	0.623	0.260	4	5	Fit

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 21015

Ability 0.726

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	.	0.009	0.354	0.492	0.141	0.003	3	4	Weakness
Data Analysis	3	0.000	0.038	0.322	0.565	0.075	4	5	Weakness
Data Gap Identification	.	0.001	0.080	0.423	0.459	0.038	4	4	Opportunity
Data Monetization	.	0.009	0.354	0.492	0.141	0.003	3	4	Weakness
Data Monetization Applications	.	0.007	0.318	0.505	0.165	0.004	3	4	Weakness
Feature Engineering	.	0.005	0.251	0.518	0.220	0.007	3	4	Weakness
Feature Engineering Advanced	3	0.009	0.346	0.496	0.146	0.003	3	4	Weakness
Model Interpretation	3	0.003	0.209	0.516	0.261	0.010	3	4	Weakness
Model Validation	3	0.000	0.049	0.357	0.533	0.060	4	5	Weakness
Programming & Coding	4	0.007	0.313	0.507	0.169	0.004	3	4	Fit
Programming & Coding Advanced	4	0.000	0.029	0.291	0.589	0.090	4	5	Weakness
Statistical Analysis	3	0.000	0.038	0.322	0.565	0.075	4	5	Weakness
Tech Savvy	4	0.000	0.072	0.409	0.477	0.042	4	4	Fit
Theoretical Quant Foundations	4	0.000	0.004	0.117	0.626	0.253	4	5	Weakness

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 21009

Ability 0.455

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	2	0.015	0.429	0.455	0.099	0.002	3	4	Weakness
Data Analysis	4	0.000	0.058	0.380	0.510	0.052	4	5	Weakness
Data Gap Identification	3	0.001	0.117	0.469	0.389	0.024	3	4	Weakness
Data Monetization	2	0.015	0.429	0.455	0.099	0.002	3	4	Weakness
Data Monetization Applications	3	0.012	0.392	0.475	0.118	0.002	3	4	Weakness
Feature Engineering	3	0.008	0.320	0.505	0.163	0.004	3	4	Weakness
Feature Engineering Advanced	3	0.014	0.421	0.460	0.103	0.002	3	4	Weakness
Model Interpretation	3	0.005	0.274	0.515	0.199	0.006	3	4	Weakness
Model Validation	3	0.000	0.075	0.413	0.471	0.041	4	5	Weakness
Programming & Coding	3	0.012	0.387	0.478	0.121	0.002	3	4	Weakness
Programming & Coding Advanced	4	0.000	0.046	0.349	0.541	0.063	4	5	Weakness
Statistical Analysis	3	0.000	0.058	0.380	0.510	0.052	4	5	Weakness
Tech Savvy	4	0.001	0.106	0.458	0.408	0.027	3	4	Fit
Theoretical Quant Foundations	4	0.000	0.008	0.156	0.639	0.197	4	5	Weakness

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 21016

Ability 0.305

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	4	0.019	0.472	0.427	0.080	0.001	2	4	Fit
Data Analysis	3	0.000	0.074	0.413	0.472	0.041	4	5	Weakness
Data Gap Identification	4	0.002	0.143	0.491	0.346	0.018	3	4	Fit
Data Monetization	3	0.019	0.472	0.427	0.080	0.001	2	4	Weakness
Data Monetization Applications	2	0.016	0.437	0.451	0.096	0.002	3	4	Weakness
Feature Engineering	3	0.010	0.364	0.488	0.135	0.003	3	4	Weakness
Feature Engineering Advanced	2	0.019	0.465	0.433	0.083	0.001	2	4	Weakness
Model Interpretation	3	0.007	0.316	0.506	0.167	0.004	3	4	Weakness
Model Validation	2	0.001	0.094	0.443	0.431	0.032	3	5	Weakness
Programming & Coding	4	0.015	0.431	0.454	0.098	0.002	3	4	Fit
Programming & Coding Advanced	4	0.000	0.060	0.383	0.506	0.050	4	5	Weakness
Statistical Analysis	3	0.000	0.074	0.413	0.472	0.041	4	5	Weakness
Tech Savvy	3	0.001	0.131	0.482	0.365	0.021	3	4	Weakness
Theoretical Quant Foundations	3	0.000	0.011	0.183	0.638	0.168	4	5	Weakness

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 21007

Ability 0.000

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	2	0.030	0.550	0.368	0.051	0.001	2	4	Weakness
Data Analysis	3	0.001	0.113	0.466	0.395	0.025	3	5	Weakness
Data Gap Identification	4	0.003	0.203	0.515	0.269	0.011	3	4	Fit
Data Monetization	2	0.030	0.550	0.368	0.051	0.001	2	4	Weakness
Data Monetization Applications	2	0.025	0.517	0.395	0.062	0.001	2	4	Weakness
Feature Engineering	2	0.017	0.447	0.444	0.091	0.001	2	4	Weakness
Feature Engineering Advanced	2	0.029	0.543	0.374	0.053	0.001	2	4	Weakness
Model Interpretation	4	0.012	0.398	0.472	0.115	0.002	3	4	Fit
Model Validation	4	0.001	0.140	0.488	0.352	0.019	3	5	Weakness
Programming & Coding	3	0.024	0.512	0.399	0.064	0.001	2	4	Weakness
Programming & Coding Advanced	3	0.001	0.093	0.442	0.432	0.032	3	5	Weakness
Statistical Analysis	3	0.001	0.113	0.466	0.395	0.025	3	5	Weakness
Tech Savvy	3	0.003	0.187	0.511	0.287	0.012	3	4	Weakness
Theoretical Quant Foundations	4	0.000	0.019	0.240	0.620	0.121	4	5	Weakness

Individual Employee Strength, Fit, Opportunity and Weaknesses

Employee 10349

Ability -0.154

Item	Selected Score	Prob. Selecting a Score of 1	Prob. Selecting a Score of 2	Prob. Selecting a Score of 3	Prob. Selecting a Score of 4	Prob. Selecting a Score of 5	Most Likely Score	Success Profile	Status
Business Intelligence Applications	2	0.038	0.585	0.337	0.040	0.000	2	4	Weakness
Data Analysis	4	0.001	0.137	0.487	0.355	0.020	3	5	Weakness
Data Gap Identification	2	0.004	0.237	0.518	0.233	0.008	3	4	Weakness
Data Monetization	2	0.038	0.585	0.337	0.040	0.000	2	4	Weakness
Data Monetization Applications	3	0.031	0.554	0.365	0.049	0.001	2	4	Weakness
Feature Engineering	2	0.021	0.487	0.417	0.073	0.001	2	4	Weakness
Feature Engineering Advanced	2	0.036	0.578	0.343	0.042	0.000	2	4	Weakness
Model Interpretation	2	0.016	0.439	0.449	0.094	0.001	3	4	Weakness
Model Validation	4	0.002	0.167	0.504	0.312	0.015	3	5	Weakness
Programming & Coding	2	0.030	0.549	0.369	0.051	0.001	2	4	Weakness
Programming & Coding Advanced	2	0.001	0.114	0.467	0.393	0.025	3	5	Weakness
Statistical Analysis	4	0.001	0.137	0.487	0.355	0.020	3	5	Weakness
Tech Savvy	4	0.003	0.220	0.517	0.250	0.009	3	4	Fit
Theoretical Quant Foundations	5	0.000	0.025	0.271	0.603	0.101	4	5	Fit