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Data Scientist Job Description

This data scientist job description covers predictive modelling, experimentation and translating business problems into machine learning solutions. It suits fintech, e-commerce, health-tech and consulting teams.

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[Company Logo][Company Name]

JOB DESCRIPTION – DATA SCIENTIST

Job Title:: Data Scientist
Department:: Data Science
Reports To:: Head of Data Science
Location:: [Work Location]
Employment Type:: Full-time, Permanent
Experience:: 3–6 years in data science or ML
Job Code:: [Reference Number]

About [Company Name]

[Company Name] is [one-line description of your business, products/services and team size]. Office address: [Company Address].

Job Summary

The Data Scientist will build and validate statistical and machine learning models that solve [Business Problems] at [Company Name], and work with engineering to put them into production with measurable impact.

Key Responsibilities

Frame business problems as data science problems with clear success metrics
Explore data and engineer features
Build, tune and validate models using Python ML libraries
Design and analyse A/B experiments
Monitor model performance and drift
Work with ML engineers to deploy models
Explain results to non-technical stakeholders
Document methods and assumptions
Ensure responsible use of personal data

Required Skills

Python, Pandas, scikit-learn
Statistics and hypothesis testing
SQL
Model evaluation
Communication

Qualifications & Experience

Degree in statistics, mathematics, CS, engineering or economics
3–6 years in data science

Preferred Skills (Good to Have)

Deep learning frameworks
Spark / big data
Domain knowledge of [Domain]

Key Performance Indicators (KPIs)

KPI	Target	Review Frequency
Models delivered to production	[Number] per half-year	Half-yearly
Business metric uplift from models	[Percentage]%	Quarterly
Model performance vs baseline	+[Percentage]%	Per model

Salary Range

₹[Minimum CTC] – ₹[Maximum CTC] per annum (CTC), depending on experience and skills. Final offer will show the fixed and variable split and approximate in-hand salary.

Benefits

Provident Fund (EPF) and ESI as applicable, gratuity as per law
[Number] days of paid leave plus public holidays as per company policy
[Health insurance / group mediclaim cover]
Conference and research budget

How to Apply

Share your CV with a summary of two models you built and their business impact. Send your application to [HR Email] with the subject line "Application – Data Scientist – [Your Name]" or apply through [Application Link]. Last date to apply: [Date].

Prepared by
[HR Name]
Human Resources
Date: [Date]

Approved by
[Reporting Manager]
Data Science
Date: [Date]

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What this template includes

A job summary that explains why the data scientist role exists and where it sits in Data Science
9 role-specific responsibilities, starting with frame business problems as data science problems with clear success metrics
Required skills such as python, pandas, scikit-learn and statistics and hypothesis testing, so screening criteria are clear
Qualifications and experience (3–6 years in data science or ML) stated up front to filter applications
A KPI table, e.g. models delivered to production, that later becomes the appraisal yardstick
Salary range, benefits and how-to-apply sections with placeholders ready to fill

When to use it

Building credit scoring, churn or demand forecasting models
Designing A/B tests and measuring impact
Creating recommendation or pricing models
Moving from descriptive reports to prediction

How to customise this template

1Replace [Company Name], [Work Location] and the reporting line (Head of Data Science) with your actual details
2Trim or reorder the responsibilities so the top three reflect what the data scientist will spend most time on
3Describe the business problems the scientist will work on and the data available
4Fill in the salary range as annual CTC in ₹ and decide whether to show it publicly or only on request
5Set realistic targets in the KPI table with the hiring manager before the role goes live

HR tips

State real use-cases, not just "AI/ML", to attract the right profile
Check for business impact in past work, not only model accuracy
Clarify who deploys models: the scientist or ML engineers

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Frequently asked questions

What does a data scientist do?+

A data scientist uses statistics and machine learning to build models that predict outcomes, run experiments and turn data into business decisions.

Which skills are required?+

Python, SQL, statistics, ML libraries such as scikit-learn and XGBoost, experimentation and communication of results.

Is a PhD needed for data science roles?+

Not usually. Most Indian industry roles ask for a relevant degree and proven project experience.

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