Senior Analyst - Data Science

Darwinbox Digital Solutions Pvt. Ltd.; India; On-site; INR 2,600,000 - 4,800,000; Full time

Job summary

Darwinbox Digital Solutions Pvt. Ltd. seeks a Senior Analyst - Data Science to lead advanced analytics across HR transformation programs. You will guide cross-functional teams, frame analytical problems, and develop explainable AI-enabled insights for workforce decisions.

You will deploy production-ready models, support experiments, and ensure responsible AI practices with bias testing and governance. Strong communication with leadership is required.

Qualifications

• Bachelor's degree in a quantitative field or related experience.
• Typically 5+ years in analytics, data science, or enterprise analytics.
• Experience leading cross-functional advisory engagements.
• Ability to frame decisions and present recommendations to senior leaders.
• Establish standards, governance, and reusable frameworks.
• Experience with statistical analysis, ML modeling, experimentation, or NLP.

Responsibilities

• Provide advisory leadership across HR Transformation & Analytics initiatives.
• Develop statistical, predictive, forecasting, NLP, or ML analyses for HR use cases.
• Document model purpose, data sources, assumptions, and risks; ensure governance.
• Translate findings into practical recommendations with measurable steps.
• Support AI/ML deployment, monitoring, and responsible AI practices.
• Shape AI adoption patterns with governance artifacts and playbooks.

Skills

Statistical analysis

ML modeling

Forecasting

NLP

Experimentation

Data storytelling

Executive communication

Ambiguity framing

Education

Bachelor's degree in Computer Science, Statistics, Data Science

Tools

Python

R

SQL

Pandas

MLflow

TensorFlow/PyTorch

Job description

The Senior Analyst - Data Science provides advanced, cross-functional advisory leadership for theHuman Resources Group (HRG). This role is used for complex, ambiguous, orenterprise-impacting work that requires deep functional expertise, strongstakeholder influence, standards-setting, and the ability to connect businessoutcomes, data, technology, process, product, and AI-readiness considerationsacross multiple teams or portfolios.

The Senior Analyst - Data Sciencesupports HRG by leading statistical analysis efforts, deployingproduction ready machine learning models, experimentation, forecasting, LargeLanguage models, and advanced analytics techniques to workforce and HRbusiness problems. This role leads the partnership with HR leaders, dataengineers, data analysts, business insights analysts, product teams, andgovernance partners to develop explainable, ethical, and actionableanalytical solutions.

Therole is responsible for framing analytical problems, preparing data, buildingand evaluating models, ML Ops, communicating findings, and supportingresponsible deployment of predictive or AI-enabled insights. This role isrequired to ensure bias and fairness testing has been completed prior to thedeployment of an AI solution. Success requires statistical rigor, businesscontext, coding capability, responsible AI awareness, and clearcommunication.

Responsibilities:

Consulting-Level Advisory & Cross-Functional Leadership

• Serve as a trusted advisor on complex HR Transformation &Analytics initiatives that span multiple stakeholders, systems, functions, orbusiness outcomes.
• Shape solution direction, operating approach, deliverystandards, analytical methods, technical patterns, or governance practicesfor assigned areas of expertise.
• Lead complex discovery, problem framing, impact analysis,stakeholder alignment, and recommendation development where ownership orsolution paths are not yet clear.
• Influence leaders and cross-functional partners throughstructured analysis, executive-ready communication, trade-off framing, andpractical implementation recommendations.
• Establish reusable practices, playbooks, quality standards,templates, and decision frameworks that improve consistency and maturityacross the HR Transformation & Analytics organization.

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.
• Datastorytelling thru compelling executive ready report outs of analyticalfindings.

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:

• Shapes responsible AI adoption patterns for assigned domains,including appropriate use cases, controls, human review expectations,validation approaches, and stakeholder readiness.
• Advises leaders and cross-functional partners on how AI canimprove productivity, insight generation, engineering, analytics delivery,process maturity, product outcomes, and workforce readiness.
• Establishes or contributes to reusable AI guidance, promptpatterns, quality checks, governance artifacts, and adoption practices thatreduce risk and improve consistency.
• Builds advanced capability in AI-enabled operating models,responsible AI governance, AI-assisted analytics and engineering, andfuture‑of‑work implications over the next three years.
• 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 5+ years of experience in a related environment.
• Experience leading complex cross‑functional initiatives,advisory engagements, enterprise analytics, technology delivery,transformation workstreams, or process improvement efforts.
• Experience influencing leaders, resolving ambiguity, framingdecisions, and presenting recommendations to senior managers, directors, orsenior directors.
• Experience establishing standards, governance practices,reusable frameworks, playbooks, or capability maturity improvements acrossteams or portfolios.
• 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

• Advanced advisory capability with the ability to frameambiguous problems, assess trade‑offs, and recommend practical enterprise orcross‑functional solutions.
• Strong executive communication skills, including the abilityto convert complex analysis, process, technology, product, or data issuesinto clear decisions and actions.
• Demonstrated ability to establish standards, governancepractices, methods, templates, or playbooks that improve consistency andmaturity across teams.
• Ability to influence senior stakeholders and cross‑functionalpartners while balancing business outcomes, risk, feasibility, scalability,and user impact.
• 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 advising executive or senior leadership audienceson complex workforce, HR, analytics, product, technology, process, ortransformation decisions.
• Experience developing capability models, roadmaps, governanceframeworks, maturity assessments, standards, or enterprise playbooks.
• Experience shaping responsible AI adoption, data governance,analytics modernization, automation strategy, or AI‑enabled workforcereadiness practices.
• Experience with HR analytics, workforce planning, talentanalytics, employee listening, retention modeling, skills analytics, or laborforecasting.
• 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.