Associate II - Data Science

Darwinbox Digital Solutions Pvt. Ltd.; India; On-site; INR 1,200,000 - 1,600,000; Full time

Job summary

Darwinbox Digital Solutions Pvt. Ltd. is seeking a Data Scientist in Advanced Analytics to collaborate with HR and analytics teams. You will apply statistical methods, ML, forecasting, and NLP to workforce challenges, framing problems, preparing data, building models, and communicating insights.

You will work with data engineers, product teams, and governance partners to ensure explainable, ethical, and actionable analytics, while supporting responsible deployment of AI-enabled insights.

Qualifications

• Bachelor's degree in CS/ML/Data Analytics or related field with relevant experience.
• 1-2 years of experience in statistics, ML, forecasting, NLP, experimentation, or advanced analytics.
• Experience using Python, R, SQL for data prep, modeling, evaluation, and visualization.
• Ability to communicate analytical findings and business implications to diverse stakeholders.
• Awareness of responsible AI concepts, fairness, explainability, and data privacy.

Responsibilities

• Develop statistical, predictive, forecasting, segmentation, classification, NLP, or ML analyses for HR/workforce use cases.
• Prepare features, evaluate model performance, document assumptions, and compare approaches based on data quality.
• Support experiments, pilots, model validations, and prototypes guiding HR decisions.
• Document model purpose, data sources, assumptions, limitations, metrics, risks, use cases.
• Ensure fairness, bias checks, explainability, privacy, and governance reviews for models.
• Translate findings into practical recommendations and measurable next steps.
• Partner with HR and data teams to frame problems and enable operationalization.

Skills

Statistics

Machine learning

Python

R

SQL

Data preprocessing

Model evaluation

Explainability

Communication

Ambiguity handling

Responsible AI

Education

Bachelor's degree in CS/ML/Data Analytics

1-2 years experience in related environment

Tools

scikit-learn

pandas

PySpark

TensorFlow

PyTorch

MLflow

Databricks

Snowflake

Job description

Manager,Advanced Analytics and Data Science

Matrixreports to (Title):

Asapplicable based on HR Transformation & Analytics portfolio alignment

DirectReports:

Created/ Last Revised:

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JobCode:

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Position Summary:

TheData Scientist supports the Human Resources Group by applying statisticalanalysis, machine learning, experimentation, forecasting, and advancedanalytics techniques to workforce and HR business problems. This rolepartners with HR leaders, data engineers, data analysts, business insightsanalysts, product teams, and governance partners to develop explainable,ethical, and actionable analytical solutions.

Therole is responsible for framing analytical problems, preparing data, featureengineering, building and evaluating models, communicating findings, andsupporting responsible deployment of predictive or AI-enabled insights.Success requires statistical rigor, business context, coding capability,responsible AI awareness, and clear communication.

Responsibilities:

Advanced Analytics & Modeling

• Develop statistical, predictive, forecasting, segmentation,classification, natural language, or machine learning analyses for HR andworkforce use cases.
• Prepare features, evaluate model performance, documentassumptions, and compare modeling approaches based on business needs and dataquality.
• Support experiments, pilots, model validations, and analyticalprototypes that inform HR decisions and transformation priorities.

Responsible AI, Evaluation & Documentation

• Document model purpose, data sources, assumptions,limitations, performance metrics, risks, and appropriate use cases.
• Support fairness, bias, explainability, privacy, andgovernance reviews for models or AI-enabled analytical outputs.
• Validate results with business stakeholders and avoidunsupported or overly deterministic interpretations of workforce data.

Insight Translation & Partnership

• Partner with HR domain experts, product analysts, businessinsights analysts, and data teams to frame problems and interpret results incontext.
• Translate advanced analytical findings into practicalrecommendations, decision options, and measurable next steps.
• Support operationalization of models, model monitoring, andfeedback loops in partnership with engineering and governance teams.

AI-Augmented Data Science

• Use approved AI tools to support code drafting, exploratoryanalysis, feature brainstorming, documentation, model comparison summaries,and research synthesis.
• Validate AI-assisted code, findings, and modelingrecommendations through reproducible methods and peer review.
• Identify opportunities where AI, machine learning, or naturallanguage analytics can responsibly improve HR insight and decision support.

AI Preparedness Expectations:

• Uses AI to accelerate code drafts, research synthesis, featureexploration, and documentation while preserving reproducibility andvalidation.
• Understands AI risk, model limitations, fairness,explainability, privacy, and appropriate human oversight forworkforce-related models.
• Builds capability in responsible AI, model operations,generative AI evaluation, decision science, and AI-enabled workforceanalytics.

Education & Experience:

• Bachelor's degree in Computer Science, Machine Learning, Data Analytics,Statistics, Engineering, Economics, or a related field; equivalent experiencemay be considered.
• Typically 1-2 years of experience in a related environment.
• Experience with statistical analysis, machine learning,predictive modeling, forecasting, NLP, experimentation, or advanced analyticsprojects.
• Experience using Python, R, SQL, or similar tools for datapreparation, modeling, evaluation, and visualization.
• Experience communicating analytical findings, modellimitations, and business implications to technical and non-technicalstakeholders.

Must Have Skills

• Working knowledge of statistics, machine learning concepts,model evaluation, and analytical problem framing.
• Programming skills in Python, R, SQL, or equivalent datascience tools.
• Ability to prepare data, engineer features, evaluate models,and document reproducible analysis.
• Understanding of responsible AI considerations such asfairness, explainability, privacy, bias, and appropriate use.
• Strong communication skills for translating technical resultsinto business-relevant insights.
• Ability to work with ambiguous business problems and structureanalytical approaches.
• Foundational AI literacy, including appropriate use ofAI-assisted coding, research, documentation, and model evaluation support.

Nice to Have Skills

• Experience with HR analytics, workforce planning, healthcareanalytics, talent analytics, employee listening, retention modeling, skillsanalytics, or labor forecasting.
• Exposure to scikit-learn, pandas, PySpark, TensorFlow,PyTorch, MLflow, Databricks, Snowflake, or similar tools.
• Experience with causal inference, experimentation, surveyanalytics, text analytics, optimization, or simulation.
• Knowledge of model governance, model cards, monitoring,responsible AI frameworks, or AI risk management.
• Prior experience in healthcare, regulated environments, orenterprise analytics teams.
• Experience deploying or operationalizing analytical models inpartnership with engineering teams.
• Experience with generative AI, LLM evaluation, prompt testing,embeddings, retrieval-augmented generation, or NLP workflows.

Licenses, Certifications & Training:

• Preferred: role-relevant certification, analytics platformtraining, data governance training, or Agile delivery training, asapplicable.
• Preferred: Responsible AI, data privacy, data security, or HRdata handling training.

Knowledge, Skills, Abilities, Behaviors:

• Demonstrates curiosity, ownership, and sound judgment whenworking with HR data, systems, processes, and stakeholders.
• Communicates status, assumptions, risks, and limitationsclearly without overstating what the data, process, or technology cansupport.
• Works collaboratively across HR, technology, analytics,product, operations, and transformation partners in a matrixed environment.
• Protects confidential HR and workforce information and followsinternal privacy, security, data governance, and compliance expectations.
• Uses AI tools with professional skepticism, validatesAI-assisted outputs, avoids entering restricted data into unapproved tools,and escalates AI or data risks appropriately.
• Maintains documentation discipline, change awareness, customerfocus, and continuous improvement mindset while balancing multiplepriorities.