This page introduces users to the Calibration tab of Eightfold (EF) Talent Acquisition (TA) Feature Usage & Outcomes Dashboard. The target audience is users of the dashboard, including the Head of Talent Acquisition, Talent Ops, Eightfold Admins, Recruiting Managers, Recruiters, and Sourcers.

Eightfold’s Calibration Attributes

Calibration consists of 5 key components:

• Titles
• Skills
• Ideal Candidates
• Work Experience Range
• Location

Calibration statuses

• Complete: It is considered as 'Complete' when the user clicks on the Confirm Calibration button on the Calibration page and does not make any other edits after (Note: Calibration can be confirmed without providing all the above key components)
• Needs Confirmation: This status is associated with positions where some or all of the above information is provided either by the user or auto calibrated but the user is yet to click on Confirm Calibration button
• Not Calibrated: This typically means that neither the title nor similar candidates nor skills is entered in the calibration page and the confirm calibration has not been clicked by the user

Calibration % is also determined based on the 5 key components mentioned above where all the attributes are stored as boolean values.

Formula

    (
    {has_calibrated_location}
    +{has_calibrated_similar_cand}
    +{has_calibrated_skills}
    +{has_calibrated_titles}
    +{has_calibrated_workexp}
    )*1.00/5

Filter controls

Dashboard filter controls can be used to refine or subset the data shown in the dashboard. For example, to select a certain date range or select a particular feature.

Filter controls are located at the top of the dashboard. Filter controls have to be read with context based on the type of tab.

Sections

• Overview: This section highlights the key metrics associated with calibration users as well as the outcomes of the calibration complete positions
• User Activity: This section highlights the average weekly and monthly calibration users, their respective retention percent and how that trends with time
• Positions Summary: This section gives you the calibration summary of all positions as well just open positions by Calibration status and Calibration % sliced across key position attributes
• Outcomes: This section highlights key outcomes such as Match Score of All Applicants, Average Days to Fill and %Hired of positions across calibration status
• Key Observations: This section helps in comparing the outcomes across Calibration %
• Wall of Fame: This section highlights the top 5 recruiters, job functions and locations with maximum % Positions calibrated
• Raw Data: This section helps in getting the calibration detail and the attributes of all the positions created in the selected time frame

Metric Definitions

Overview

Calibration Confirm Users

• Description: Users who have clicked on confirm calibration at least once for the positions created in the selected timeframe
• Formula in Quicksight calculation:
  • distinct_count(calibration_confirm_user)
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Positions Calibrated

• Description: Unique number of positions whose calibration status is confirmed at a point in time
• Formula in Quicksight calculation:
  • distinct_countIf({position_id}, {position_calibration_status}='Complete')
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Avg Match Score

• Description: The average match score of all applicants of all positions whose calibration status is confirmed
• Formula in Dataset:
  • avg(application_match_score)
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

% Hired

• Description: The average hired to applicants ratio of all positions whose calibration is confirmed at that point in time
• Formula in Quicksight calculation:
  • {total_hired_applicants}/{total_applicants}
    • {total_hired_applicants} = count(distinct case when stage_group = 'hired' then profile_id end) as total_hired_applicants,
    • {total_applicants} = count(distinct profile_id)
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Avg Days to Hire

• Description: The average days between application submission date and the date on which a candidate is advanced to the hired stage
• Formula in Quicksight calculation:
  • sum({sum_days_to_hire})/sum({total_hired_applicants})
    • {sum_days_to_hire} = sum(datediff('day',application_ts,case when stage_group ='hired' then hired_advanced_ts end)) as sum_days_to_hire
    • {total_hired_applicants} = count(distinct case when stage_group = 'hired' then profile_id end) as total_hired_applicants,
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

% Employees using Calibration

• Description: The ratio of employees who have clicked on ‘Confirm Calibration’ at least once till date.
• Formula: Refer to the logic in the dataset
• Dataset(s): ta_feature_calibration_breadth_of_usage_across_users

% Recruiters using Calibration

• Description: The percentage of recruiters who have clicked on ‘Confirm Calibration’ at least once among all the employees who have recruiter role mapped to them
• Formula: Refer to the logic in the dataset
• Dataset(s): ta_feature_calibration_breadth_of_usage_across_users

% Hiring Managers using Calibration

• Description: The percentage of hiring managers who have clicked on ‘Confirm Calibration’ at least once among all the employees who have hiring manager role mapped to them
• Formula: Refer to the logic in the dataset
• Dataset(s):ta_feature_calibration_breadth_of_usage_across_users

User Activity

Weekly Adoption

• Avg Weekly Active users: It is the average number of Weekly active users clicking on ‘confirm calibration’ in the selected time frame
  • YoY metric: denotes the % change in Average Weekly Active Users compared to the previous year

• Avg Weekly Retention (Repeat Users) %: It denotes the percentage of users in a given week who were active in the previous week as well

  • YoY metric: denotes how the Avg Weekly Repeat users % has changed compared to the previous year.

• Dataset(s): tafeaturecalibrationadoptionby_week

Monthly Activity

• Avg monthly Active users: It is the average number of monthly active users clicking on ‘confirm calibration’ in the selected time frame
  • YoY metric: denotes the % change in Average monthly Active Users compared to the previous year

• Avg monthly Retention (Repeat Users) %: It denotes the percentage of users in a given month who were active in the previous month as well

  • YoY metric: denotes how the Avg monthly Repeat users % has changed compared to the previous year.

• Dataset(s): tafeaturecalibrationadoptionby_month

Positions Summary

This section gives you the calibration summary of all positions as well as Open positions by Calibration status and Calibration % sliced across key position attributes

(A) By Calibration Status

Positions Calibrated

• Description: The number and % of positions has the status of ‘calibration complete’ among the positions created in the selected time frame.
• Use case: At a given time, the end user would be able to get a quick peek into how many positions are yet to be calibrated. This view can be sliced and diced at different position attributes (such as Position recruiter, hiring manager, etc.) as well as user attributes (such as Manager of the recruiter of the position)
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Calibration Status: All positions created from <date> to <date>

• Description: This pie chart gives you the position distribution across the calibration status (as of today) of the positions created in the selected time frame
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Calibration Status of All Positions by Position Created Month

• Description: This chart gives you the trended view of the positions created in every month and how the calibration looks like as of today
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Unique Positions across Position & User Attributes

• Description: This table helps you get the positions distribution by calibration status across different position and user attributes
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Open Positions Calibrated

• Description: The number and % of Open positions has the status of ‘calibration complete’ among the positions created in the selected time frame.
• Use case: At a given time, the end user would be able to get a quick peek into how many Open positions are yet to be calibrated. This view can be sliced and diced at different position attributes (such as Position recruiter, hiring manager, etc.) as well as user attributes (such as Manager of the recruiter of the position)
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Calibration Status: Open positions created from <date> to <date>

• Description: This pie chart gives you the Open positions distribution across the calibration status (as of today) of the positions created in the selected time frame
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Calibration Status of Open Positions by Position Created Month

• Description: This chart gives you the trended view of the positions created every month and with status as ‘Open’ and how the calibration status looks like as of today
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Unique Open Positions across Job Function

• Description: This table helps you get the Open positions (status as ‘Open’ as of today) distribution by calibration status (as of today) across different position and user attributes
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

(B) By Calibration %

This section is the same as the above section but the Position stats are shown across the Calibration % as of today.

Outcomes

Avg Match Score for Calibration complete

• Description: The average match score of all applicants who have applied to the calibration complete positions created during a selected time period
• Formula in Quicksight calculation:
  • avgIf({avg_match_score_all_applicants}, {position_calibration_status} = 'Complete')
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Avg Match Score for Calibration Needs Confirmation

• Description: The average match score of all applicants who have applied to the positions whose calibration needs to confirmed and created during a selected time period
• Formula in Quicksight calculation:
  • avgIf({avg_match_score_all_applicants}, {position_calibration_status} = 'Needs Confirmation')
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Avg Match Score of All Applicants across Calibration Status by Position created month

• Description: This line chart gives you an idea of how the avg match score of applicants varies across the calibration confirm positions created every month

• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Avg Match Score across Position & User Attributes

• Description: This view gives an idea of how the match score varies across Position attributes and Calibration Status

• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Hired % for Calibration complete

• Description: This refers to the average of % candidates hired among all the applicants for the calibration completed positions
• Formula in Quicksight calculation:
  • avgIf({Applicants to Hires %}, {position_calibration_status} = 'Complete') where, Applicants to Hires % = {total_hired_applicants}/{total_applicants}

• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Hired % for Calibration Needs Confirmation

• Description: This refers to the average of % candidates hired among all the applicants for the positions which needs confirmation on calibration (the ‘calibration confirm’ button has not been clicked yet)
• Formula in Quicksight calculation:
  • avgIf({Applicants to Hires %}, {position_calibration_status} = 'Needs Confirmation') where, Applicants to Hires % = {total_hired_applicants}/{total_applicants}
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Hired % across Calibration Status by Position created month

• Description: This graph gives an idea of how Hired % varies with calibration status for the positions created in every month
• Formula in Quicksight calculation:
  • Applicants to Hires % = {total_hired_applicants}/{total_applicants}
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Hired % across Job Function (or the Position or User Attribute selected)

• Description: This table shows the Hired % variation across different position and user attributes split by calibration status
• Formula in Quicksight calculation:
  • Applicants to Hires % = {total_hired_applicants}/{total_applicants}
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Avg Days to Fill for Calibration complete

• Description: This refers to the average days to fill (days between position creation and its first hire) for all calibration confirmed positions
• Formula in Quicksight calculation:
  • avgIf({days_to_fill}, {position_calibration_status}='Complete')
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Avg Days to Fill for Calibration Needs Confirmation

• Description: This refers to the average days to fill (days between position creation and its first hire) for all positions whose calibration needs to be confirmed
• Formula in Quicksight calculation:
  • avgIf({days_to_fill}, {position_calibration_status}='Needs Confirmation')

• Dataset(s): tafeaturecalibrationefficiencyand_outcomes

Avg Days to Fill across Calibration Status by Position created month

• Description: This graph gives an idea of how Average days to fill varies with calibration status for the positions created in every month
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Avg Days to Fill across Job Function

• Description: This graph gives an idea of how average days to fill varies with calibration status across position and user attributes
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Key Observations

% Recruiters with Hires by Calibration percent

• Description: This graph denotes how the % Recruiters with hires varis across Calibration %
• For example: Consider the graph below: 67% of the recruiters who have positions created in the selected time frame and with 100% calibration (calibration % definition here) have at least 1 hire
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Avg Match score of Offered, Hired & All Applicants across Position Calibration %

• Description: This graph helps in comparing the Match score of hired applicants and applicants with an offer across Calibration %
• For example: In the graph below, the match score of all applicants (red line) seems to increase with calibration % increase
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Avg Days to Fill distribution by Calibration %

• Description: This graph shows how the days to fill is distributed statistically (median, min, max, Q1, Q3 percentile) across different calibration % buckets
• For example: In the graph below, the median days to fill for a 20% calibrated position (second screenshot) is 126 vs 83 for a 100% calibrated position (third screenshot)
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Wall of Fame

Top 5 Recruiters with maximum % Positions calibrated

• Description: This shows the top 5 recruiters with maximum % positions calibrated
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Top 5 Manager of Recruiters with maximum % Positions calibrated

• Description: This shows the top 5 Manager of Recruiters with maximum % positions calibrated
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Top 5 Locations with maximum % Positions calibrated

• Description: This shows the top 5 locations mapped to positions with maximum % positions calibrated
• Dataset(s): ta_feature_calibration_efficiency_and_outcomes

Raw Data

This section has its own filters to filter relevant data and download the raw data. The raw data contains all the details of the positions, users who calibrated it and the manager of the recruiters of the position.

Datasets

ta_feature_calibration_breadth_of_usage _across_users

SELECT 

count(distinct t1.employee_email) as total_employees,
count(distinct case when (t2.timestamp IS NOT NULL)  then t1.employee_email end) as total_active_employees_confirming_calibration,

count(distinct case when is_recruiter then t1.employee_email end) as total_recruiters,
count(distinct case when is_recruiter AND (t2.timestamp IS NOT NULL)  then t1.employee_email end) as total_active_recruiters_confirming_calibration,

count(distinct case when is_hm then t1.employee_email end) as total_hiring_managers,
count(distinct case when is_hm AND (t2.timestamp IS NOT NULL)  then t1.employee_email end) as total_active_hiring_managers_confirming_calibration,

count(distinct case when (t2.timestamp IS NOT NULL) and  is_auto_calibrated then t1.employee_email end) as total_active_employees_calibrating_autocalibrated_positions,

count(distinct case when (t2.timestamp IS NOT NULL) and  is_auto_calibrated  then t1.employee_email end) *1.00/count(distinct t1.employee_email) as percent_active_employees_calibrating_autocalibrated_positions,

count(distinct case when is_recruiter  AND (t2.timestamp IS NOT NULL) and  is_auto_calibrated then t1.employee_email end) as total_active_recruiters_calibrating_autocalibrated_positions,

count(distinct case when is_recruiter  AND (t2.timestamp IS NOT NULL) and  is_auto_calibrated then t1.employee_email end) *1.00/count(distinct t1.employee_email) as percent_active_recruiters_calibrating_autocalibrated_positions


FROM 
(
          select employee_email, profile_id, hiring_date, is_recruiter, is_hm
          from volkscience_analytics.volkscience_employee 
          WHERE is_alumni = FALSE
          and termination_date IS NULL
)t1
LEFT JOIN
(
SELECT 
event,
user_id,
position_id, 
timestamp
FROM volkscience_analytics.volkscience_user_analytics
where event = 'calibration_confirmed'
group by 1, 2, 3, 4
)t2
on t1.employee_email = t2.user_id
and t1.hiring_date <= t2.timestamp

LEFT JOIN
(
SELECT position_id, is_auto_calibrated FROM volkscience_analytics.volkscience_positions group by 1, 2
)t3
on t2.position_id = t3.position_id

ta_feature_calibration_adoption_by_week

With calibration_confirm_activity_log AS
(SELECT 
datepart('year',timestamp) as year,
datepart('week',timestamp) as week,
DATE_TRUNC('week' , timestamp) as start_of_week,
event as activity_type,
user_id as active_user_id,
timestamp as activity_timestamp,
LAG(timestamp ) OVER (PARTITION BY active_user_id ORDER BY timestamp) AS previous_activity_timestamp_ts
FROM 
volkscience_analytics.volkscience_user_analytics 
where (event ='calibration_confirmed')
)

select
year,
week,
start_of_week,
activity_type,
count(distinct active_user_id) as active_users_this_week,
count(activity_timestamp) as feature_usage,
count(activity_timestamp) /count(distinct active_user_id) as frequency_of_usage_per_user,
count(DISTINCT CASE WHEN (DATEDIFF(year, previous_activity_timestamp_ts, activity_timestamp) = 0 AND DATEDIFF(week, previous_activity_timestamp_ts, activity_timestamp) = 1) OR (DATEDIFF(year, previous_activity_timestamp_ts, activity_timestamp) = 1 AND DATEDIFF(week, previous_activity_timestamp_ts, activity_timestamp) = -51)THEN active_user_id END) 
as repeat_users_following_week,
count(DISTINCT CASE WHEN DATEDIFF(week, previous_activity_timestamp_ts, activity_timestamp) = 1 THEN active_user_id END)*1.00 /count(distinct active_user_id) as repeat_users_percent
FROM
calibration_confirm_activity_log 

group by 1,2,3,4

Ta_feature_calibration_adoption_by_month

With calibration_confirm_activity_log AS
(SELECT 
datepart('year',timestamp) as year,
datepart('month',timestamp) as month,
DATE_TRUNC('month' , timestamp) as start_of_month,
event as activity_type,
user_id as active_user_id,
timestamp as activity_timestamp,
LAG(timestamp ) OVER (PARTITION BY active_user_id ORDER BY timestamp) AS previous_activity_timestamp_ts
FROM 
volkscience_analytics.volkscience_user_analytics 
where (event ='calibration_confirmed')
)

select
year,
month,
start_of_month,
activity_type,
count(distinct active_user_id) as active_users_this_month,
count(activity_timestamp) as feature_usage,
count(activity_timestamp) /count(distinct active_user_id) as frequency_of_usage_per_user,
count(DISTINCT CASE WHEN (DATEDIFF(year, previous_activity_timestamp_ts, activity_timestamp) = 0 AND DATEDIFF(month, previous_activity_timestamp_ts, activity_timestamp) = 1) OR (DATEDIFF(year, previous_activity_timestamp_ts, activity_timestamp) = 1 AND DATEDIFF(month, previous_activity_timestamp_ts, activity_timestamp) = -51)THEN active_user_id END) 
as repeat_users_following_month,
count(DISTINCT CASE WHEN DATEDIFF(month, previous_activity_timestamp_ts, activity_timestamp) = 1 THEN active_user_id END)*1.00 /count(distinct active_user_id) as repeat_users_percent
FROM
calibration_confirm_activity_log 

group by 1,2,3,4

Ta_feature_calibration_efficiency_and_outcomes (to be updated after MV is created)

SELECT 
t1.position_id,
ats_job_id,
 (
    case when trim(title) = '' 
    OR trim(title) = 'Unknown' then 'Title Not Assigned' when title IS NULL then 'Others' else title end
  ) as position_title, 
  (
    case when trim(location) = '' 
    OR trim(location) = 'Unknown' then 'Location Not Assigned' when location IS NULL then 'Others' else location end
  ) as position_location, 
  (
    case when trim(location_country) = '' 
    OR trim(location_country) = 'Unknown' then 'Country Not Assigned' when location_country IS NULL then 'Others' else location_country end
  ) as position_location_country, 
  (
    case when trim(t1.status) = '' 
    OR trim(t1.status) = 'Unknown' then 'Status Not Assigned' when t1.status IS NULL then 'Others' else t1.status end
  ) as position_status, 
  (
    case when seniority_level IS NULL then 'Others' when seniority_level = 'N/A' then 'Seniority Level Not Inferred' else seniority_level end
  ) as position_inferred_seniority_level, 
  (
    case when trim(job_function) = '' 
    OR trim(job_function) = 'Unknown' then 'Job Function Not Assigned' when job_function IS NULL then 'Others' else job_function end
  ) as position_job_function, 
  (
    case when trim(business_unit) = '' 
    OR trim(business_unit) = 'Unknown' then 'Business Unit Not Assigned' when business_unit IS NULL then 'Others' else business_unit end
  ) as position_business_unit, 
  (
    case when trim(employment_type) = '' 
    OR trim(employment_type) = 'Unknown' then 'Employment Type Not Assigned' when employment_type IS NULL then 'Others' else employment_type end
  ) as position_employment_type, 
  (
    case when trim(calibration_status) = '' 
    OR calibration_status IS NULL then 'Others' else calibration_status end
  ) as position_calibration_status, 
  (
    case when trim(position_type)= '' then 'Position Type Not Assigned' when position_type IS NULL then 'Others' else position_type end
  ) as position_type, 
  coalesce(
    recruiter, 'Recruiter Not Assigned'
  ) as position_recruiter, 
  coalesce(
    hiring_manager, 'Hiring Manager Not Assigned'
  ) as position_hiring_manager, 
  (
    case when ( trim(recruiter_name) = '' AND trim(recruiter) = '') OR recruiter IS NULL then 'Recruiter Not Assigned' 
               when (trim(recruiter_name) = '' OR trim(recruiter_name) IS NULL OR recruiter_name = 'NULL')  AND recruiter IS NOT NULL then recruiter else recruiter_name end
  ) as position_recruiter_name, 
  (
    case when (
      trim(hiring_manager_name) = '' 
      AND trim(hiring_manager) = ''
    ) 
    OR hiring_manager IS NULL then 'Hiring Manager Not Assigned' when (
      trim(hiring_manager_name) = '' 
      OR trim(hiring_manager_name) IS NULL 
      OR trim(hiring_manager_name) = 'NULL'
    ) 
    AND hiring_manager IS NOT NULL then hiring_manager else hiring_manager_name end
  ) as position_hiring_manager_name, 
  (
    case when supervisory_org IS NULL then 'Others' when supervisory_org = 'Unknown' 
    OR supervisory_org = '' then 'Supervisory Org Not Assigned' else supervisory_org end
  ) as position_supervisory_org, 
  t1.is_open as position_is_open, 
  t1.is_posted as position_is_posted, 
  t1.creation_ts as position_creation_ts, 
  t1.is_confidential as position_is_confidential, 
  t1.number_of_openings, 
  t1.earliest_opened_at as position_earliest_opened_at, 
  t1.pay_rate_type as position_pay_rate_type,

location_latitude as position_location_latitude,
location_longitude as position_location_longitude,

last_modified_ts as position_last_modified_ts,
is_priority as position_is_priority,
calibration_template_id,
workflow_template_id,

confirm_calibration_ts as postion_confirm_calibration_ts,
concat((has_calibrated_location_indicator + has_calibrated_skills_indicator  + has_calibrated_titles_indicator  + has_calibrated_similar_cand_indicator  + has_calibrated_workexp_indicator )*100/5,'%') as calibration_percent, 
calibration_json,
custom_fields_json,
skills_json,
metadata_json,
is_auto_calibrated,
case when t3.user_id IS NOT NULL then coalesce(t5.employee_name, t3.user_id) else 'calibration confirm not clicked' end as calibration_confirm_user,
case when t3.user_id IS NOT NULL then recruiter_role_indicator else 'calibration confirm not clicked' end as recruiter_role_indicator_of_calibration_confirm_user,
case when t3.user_id IS NOT NULL then hiring_manager_role_indicator else 'calibration confirm not clicked' end as hiring_manager_role_indicator_of_calibration_confirm_user,
case when t3.user_id IS NOT NULL then sourcer_role_indicator else 'calibration confirm not clicked' end as sourcer_role_indicator_of_calibration_confirm_user,
manager_of_recruiter,

t4.total_applications,
t4.total_applicants,
t4.total_applicants_phonescreened,
t4.total_applicants_interviewed,
t4.total_offered_applicants,
t4.total_hired_applicants,

t4.avg_match_score_all_applicants,
t4.avg_match_score_all_offered_applicants,
t4.avg_match_score_all_hired_applicants,
t4.sum_days_to_hire,
datediff('day',position_creation_ts, confirm_calibration_ts) as days_taken_to_calibrate,
datediff('day',position_creation_ts, min_hired_advanced_ts) as days_to_fill
FROM
(
SELECT *, 
 case when has_calibrated_location = 'True' then 1 else 0 end as has_calibrated_location_indicator,
 case when has_calibrated_skills = 'True' then 1 else 0 end as has_calibrated_skills_indicator,
 case when has_calibrated_titles = 'True' then 1 else 0 end as has_calibrated_titles_indicator,
 case when has_calibrated_similar_cand = 'True' then 1 else 0 end as has_calibrated_similar_cand_indicator,
 case when has_calibrated_workexp = 'True' then 1 else 0 end as has_calibrated_workexp_indicator

FROM volkscience_analytics.volkscience_positions ) t1

LEFT JOIN
(
SELECT 
user_id, 
position_id
FROM 
volkscience_analytics.volkscience_user_analytics 
where (event ='calibration_confirmed')
group by 1,2
)t3
on t1.position_id = t3.position_id
LEFT JOIN 
(
    select
    position_id,
    count(distinct application_id) as total_applications,
    count(distinct profile_id) as total_applicants,
    count(distinct case when stage_group = 'phonescreen' then profile_id end) as total_applicants_phonescreened,
    count(distinct case when stage_group = 'onsite' OR  stage_group = 'interview' then profile_id end) as total_applicants_interviewed,
    count(distinct case when stage_group = 'offer' then profile_id end) as total_offered_applicants,
    count(distinct case when stage_group = 'hired' then profile_id end) as total_hired_applicants,
    
    avg(application_match_score) as avg_match_score_all_applicants,
    avg(case when stage_group = 'offer' then application_match_score end) as avg_match_score_all_offered_applicants,
    avg(case when stage_group = 'hired' then application_match_score end) as avg_match_score_all_hired_applicants,
    sum(datediff('day',application_ts,case when stage_group ='hired' then hired_advanced_ts end)) as sum_days_to_hire,
    min(hired_advanced_ts) min_hired_advanced_ts
from volkscience_analytics.volkscience_ta_leaders_hiring_outcomes
group by 1
)t4
on t1.position_id = t4.position_id

LEFT JOIN
(
SELECT 
employee_email,
concat(first_name, concat(' ',last_name)) as employee_name,
case when is_recruiter then 'Yes' else 'No' end as recruiter_role_indicator,
case when is_hm then 'Yes' else 'No' end as hiring_manager_role_indicator,
case when is_sourcer then 'Yes' else 'No' end as sourcer_role_indicator,
manager_name as manager_of_recruiter
FROM 
volkscience_analytics.volkscience_employee
)t5
on trim(lower(t1.recruiter)) = trim(lower(t5.employee_email))

Calbration FAQ's

What will be the calibration status of a position when I edit the calibration and don’t click on the calibration confirm button?

‘Needs Confirmation’ will be the calibration status of these positions.

Why is the total number of positions in Calibration Dashboard not matching the Positions UI?

• The total number of positions in Positions UI does not have any default filters. However, Calibration dashboard shows:

  • positions created in the Last 36 months
  • position_type = ATS
  • non confidential positions

• Some of the filters available in Calibration dashboard is not the same as the ones available in Positions UI

  • (Example; 'well_calibrated' filter is available in Positions UI vs not available in Calibration dashboard)
  • There is no 'position creation date' filter available in Positions UI

• Gap in Data refresh also might add to the discrepancy in few cases