Datasets:

asadullahdogarr
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adaption-corpintel-attrition-reasoning-v1

JobRole string	Attrition bool	enhanced_prompt string	enhanced_completion string	reasoning_trace string	Age int64	BusinessTravel string	DailyRate int64	Department string	DistanceFromHome int64	Education int64	EducationField string	EmployeeCount int64	EmployeeNumber int64	EnvironmentSatisfaction int64	Gender string	HourlyRate int64	JobInvolvement int64	JobLevel int64	JobSatisfaction int64	MaritalStatus string	MonthlyIncome int64	MonthlyRate int64	NumCompaniesWorked int64	Over18 string	OverTime bool	PercentSalaryHike int64	PerformanceRating int64	RelationshipSatisfaction int64	StandardHours int64	StockOptionLevel int64	TotalWorkingYears int64	TrainingTimesLastYear int64	WorkLifeBalance int64	YearsAtCompany int64	YearsInCurrentRole int64	YearsSinceLastPromotion int64	YearsWithCurrManager int64	original_context string
Laboratory Technician	null	Analyze the following raw job profile data and present it as a clear, structured summary. Identify the role, travel requirements, and interpret the associated numerical metrics and boolean flag based on standard hiring data conventions. Data Input: Laboratory Technician Travel_Rarely 15 1 3 2207 false 2 28	# Employee Telemetry Analysis & Attrition Risk Assessment ## I. Structured Data Summary & Interpretation The following table maps the raw input data into a standardized professional profile based on standard human resources information system (HRIS) conventions for laboratory and technical staffing. \| Raw Data Point...	Alright, let's break down this prompt carefully. I'm being asked to act as an elite HR Business Partner AI analyzing employee telemetry to model attrition risk. There's a lot of layered instructions here, so I need to make sure I hit every single constraint. First, the system prompt tells me I must: 1. Do a multi-vari...	28	Travel_Rarely	895	Research & Development	15	2	Life Sciences	1	1,102	1	Male	50	3	1	3	Divorced	2,207	22,482	1	Y	false	16	3	4	80	1	4	5	2	4	2	2	2	Travel_Rarely 15 1 3 2207 false 2 28
Research Scientist	null	Analyze the following job profile data and provide a summary of the candidate's suitability based on the provided metrics: <job_profile_data> Research Scientist Travel_Rarely 4 3 3 2996 true 3 37 </job_profile_data>	# Employee Telemetry and Retention Viability Assessment ## 1. Executive Summary and Data Integrity Validation The following report provides a comprehensive evaluation of the subject employee profile, designated as a Research Scientist. The objective is to determine long-term suitability by modeling attrition risk base...	Okay, let me parse this input. I'm looking at a data stream for a Research Scientist who rarely travels, followed by a raw numerical sequence: 4, 3, 3, 2996, true, 3, 37. I need to act as an elite HR Business Partner AI, but my underlying methodology must reflect a deep, forensic approach to fragmented data—distinguish...	37	Travel_Rarely	1,439	Research & Development	4	1	Life Sciences	1	1,394	3	Male	54	3	1	3	Married	2,996	5,182	7	Y	true	15	3	4	80	0	8	2	3	6	4	1	3	Travel_Rarely 4 3 3 2996 true 3 37
Research Director	null	Analyze the following job listing data and provide a structured summary of the role, including title, travel requirements, and key metrics. --- Job Listing Data: Research Director Non-Travel 1 1 4 13603 true 3 37 --- Please interpret the numerical and boolean values in the context of a job posting (e.g., years...	# Workforce Telemetry and Requisition Structural Analysis ## Executive Summary of Data Ingestion The provided dataset represents a fragmented metadata stream associated with a specific job requisition. To derive actionable intelligence from this heterogeneous data structure, a rigorous parsing protocol has been applie...	Okay, let me parse this prompt carefully. I have a dual mandate here. On one hand, I need to act as an elite HR Business Partner AI, modeling attrition risk through multi-variable analysis and concluding with a managerial intervention and a final attrition target. On the other hand, I have strict data integrity constra...	37	Non-Travel	728	Research & Development	1	4	Medical	1	380	1	Female	80	3	3	4	Divorced	13,603	11,677	2	Y	true	18	3	1	80	2	15	2	3	5	2	0	2	Non-Travel 1 1 4 13603 true 3 37
Sales Executive	null	Analyze the following candidate profile data and provide a structured summary of the applicant's suitability based on the provided metrics. Candidate Data: - Role: Sales Executive - Travel Requirement: Travel_Rarely - Metric 1: 8 - Metric 2: 3 - Metric 3: 3 - Metric 4: 7264 - Boolean Flag: false - Metric 5: 4 - Metric...	# Comprehensive Candidate Suitability and Attrition Risk Analysis ## Executive Summary and Telemetry Mapping This report evaluates the heterogeneous telemetry provided for the Sales Executive candidate. The assessment integrates financial compensation, commute friction, career velocity, and localized job satisfaction ...	Okay, I need to analyze this candidate profile. Let me break down what I'm looking at. I'm an HR Business Partner AI, so my primary lens here is attrition risk modeling based on heterogeneous employee telemetry. The prompt also asks for a structured summary of suitability. First, let's decode these raw metrics. The da...	30	Travel_Rarely	231	Sales	8	2	Other	1	982	3	Male	62	3	3	3	Divorced	7,264	9,977	5	Y	false	11	3	1	80	1	10	2	4	8	4	7	7	Travel_Rarely 8 3 3 7264 false 4 30
Manufacturing Director	null	Analyze the following raw data record representing a job profile. Extract and structure the information into a clear, readable format (such as JSON or a labeled list), identifying fields such as Job Title, Travel Requirements, and associated numerical metrics. Preserve all original values exactly as they appear. Raw D...	# Employee Telemetry Profile: Structural Extraction and Attrition Risk Assessment ## 1. Data Structuring and Field Identification The following section delineates the raw telemetry record into a structured schema. Each data point has been mapped to its corresponding human capital metric based on standard organization...	Alright, I need to break down this raw data record and structure it, then perform an attrition risk analysis. Let's start by looking at the raw data provided: Manufacturing Director Travel_Rarely 8 2 1 5121 false 3 34 First, I need to map these values to standard HR telemetry fields. The prompt asks me to extract and...	34	Travel_Rarely	167	Research & Development	8	5	Life Sciences	1	775	2	Female	32	3	2	1	Divorced	5,121	4,187	3	Y	false	14	3	3	80	1	7	3	3	0	0	0	0	Travel_Rarely 8 2 1 5121 false 3 34
Laboratory Technician	null	Analyze the following raw job profile data and present it as a structured, readable summary. Please interpret the numerical and boolean values based on standard job listing conventions (e.g., years of experience, salary, travel frequency, benefits status). Raw Data: Laboratory Technician Travel_Rarely 28 2 3 5207 ...	# Executive Profile Summary: Laboratory Technician Incumbent The following structured summary interprets the provided raw data points as a composite employee telemetry profile. This interpretation aligns with standard human capital metrics used to assess role stability, compensation competitiveness, and operational fr...	Alright, let's break down this raw data. I'm presented with a list of disjointed values for a Laboratory Technician, and I need to interpret them using standard job listing conventions. The data points are: Laboratory Technician, Travel_Rarely, 28, 2, 3, 5207, true, 3, 33. First, I'll map these to standard HR telemetr...	33	Travel_Rarely	589	Research & Development	28	4	Life Sciences	1	1,549	2	Male	79	3	2	3	Married	5,207	22,949	1	Y	true	12	3	2	80	1	15	3	3	15	14	5	7	Travel_Rarely 28 2 3 5207 true 3 33
Laboratory Technician	null	Analyze the following raw job listing data, which appears to be unstructured or delimited by newlines. Interpret the fields (such as Job Title, Travel Requirements, and numerical metrics) and present the information in a clear, structured format. Raw Data: Laboratory Technician Travel_Rarely 10 3 1 3452 false 3 ...	### Comprehensive Job Listing Data Interpretation and Retention Risk Analysis The following analysis decodes the provided unstructured dataset into a standardized human resources information system (HRIS) format. This process involves interpreting delimited numerical and categorical fields to establish a clear profile...	Alright, let me break down this prompt. I have raw, unstructured data for a job listing, and I need to interpret the fields, structure it, and then apply a multi-variable attrition risk analysis. I also need to act as a senior laboratory career counselor with a strong HR analytics background, though I can't explicitly ...	37	Travel_Rarely	921	Research & Development	10	3	Medical	1	486	3	Female	98	3	1	1	Married	3,452	17,663	6	Y	false	20	4	2	80	1	17	3	3	5	4	0	3	Travel_Rarely 10 3 1 3452 false 3 37
Sales Executive	null	Analyze the following candidate profile data and provide a structured summary of the key attributes, including role, travel requirements, and numerical metrics. Candidate Data: Sales Executive Travel_Rarely 2 4 3 9602 true 2 37	# Comprehensive Workforce Telemetry & Attrition Risk Assessment ## 1. Executive Data Summary & Schema Interpretation The following analysis dissects the provided employee telemetry profile to determine stability, performance alignment, and attrition risk. In high-velocity commercial environments, raw data points must ...	Alright, let me break down this request. I'm looking at raw candidate data that I need to interpret and analyze from an HR analytics and sales operations perspective. The data is disjointed, so my first task is to map these raw numbers and values to standard workforce metrics. The role is "Sales Executive" and the tr...	37	Travel_Rarely	558	Sales	2	3	Marketing	1	656	4	Male	75	3	2	3	Married	9,602	3,010	4	Y	true	11	3	3	80	1	17	3	2	3	0	1	0	Travel_Rarely 2 4 3 9602 true 2 37
Research Scientist	null	Analyze the following structured data record representing a job profile and its associated metrics. Interpret the fields based on the context of a 'Research Scientist' role where travel frequency is 'Rarely', and provide a summary or analysis of what these numerical and boolean values likely represent (e.g., years of e...	### Executive Telemetry Reconstruction and Attrition Risk Modeling The provided data record represents a fragmented employee telemetry snapshot typical of Human Capital Management (HCM) systems. To derive actionable intelligence, each discrete data point must be mapped to standard HR analytics schema fields. Based on ...	I need to parse this fragmented data record for a Research Scientist who rarely travels. Let me break down these raw values and map them to standard HR telemetry fields. The values are: 10, 4, 1, 2782, true, 2, 37. Let me think about what these represent in a typical HCM system schema: - 10: Likely Total Working Year...	37	Travel_Rarely	571	Research & Development	10	1	Life Sciences	1	802	4	Female	82	3	1	1	Divorced	2,782	19,905	0	Y	true	13	3	2	80	2	6	3	2	5	3	4	3	Travel_Rarely 10 4 1 2782 true 2 37
Research Scientist	null	"Analyze the following structured data record representing a professional profile. Interpret the fie(...TRUNCATED)	"### Executive Telemetry Summary and Profile Decoding\n\nThe provided data record represents a fragm(...TRUNCATED)	"Alright, let me break down this data record. I'm looking at a sequence of values for a professional(...TRUNCATED)	34	Travel_Rarely	181	Research & Development	2	4	Medical	1	1,755	4	Male	97	4	1	4	Married	2,932	5,586	0	Y	true	14	3	1	80	3	6	3	3	5	0	1	2	Travel_Rarely 2 4 4 2932 true 3 34

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This dataset is a remastered version prepared using Adaption's Adaptive Data platform.

adaption-CorpIntel-Attrition-Reasoning-v1

Instruction-tuned HR dataset engineered to evaluate employee flight risk. Replaces basic binary classification with deep reasoning traces that analyze multi-variable telemetry (commute friction, overtime, income, and job satisfaction) to generate actionable Managerial Intervention Plans.