Data Scientist

Vrinda International; Gurugram District; On-site; INR 1,200,000 - 2,200,000; Full time

Job summary

MCube Financial LLC in Gurugram is hiring a Data Scientist to develop and implement machine learning and data science solutions. You will analyze large datasets, build predictive models, and collaborate with business and technical teams to translate analytics into actionable insights.

The role requires 3–5 years of experience in data science, proficiency in Python, SQL, and statistical methods, and strong problem-solving abilities.

Qualifications

• Experience: 3–5 Years
• Location: Gurugram
• Notice Period: 15–30 Days

Responsibilities

• Develop and implement machine learning and data science solutions.
• Analyze large and complex datasets to identify meaningful patterns and insights.
• Build, train and evaluate predictive models.
• Perform data preprocessing, feature engineering and statistical analysis.
• Collaborate with business and technical teams to understand requirements.
• Translate business problems into data‑driven solutions.
• Visualize and communicate analytical findings to stakeholders.
• Monitor model performance and improve solutions based on business requirements.

Skills

Data Science

Python

Statistical Analysis

Predictive Modeling

Data Analysis

Machine Learning Algorithms

SQL

Problem Solving

Job description

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Job Description

Hiring | Data Scientist

MCube Financial LLC is hiring a Data Scientist for its Gurugram team.

• Experience: 3–5 Years
  
• Location: Gurugram
  
• Notice Period: 15–30 Days
  

Key Skills Required

• Data Science
  
• Python
  
• Statistical Analysis
  
• Predictive Modeling
  
• Data Analysis
  
• Machine Learning Algorithms
  
• SQL
  
• Problem Solving
  

Key Responsibilities

• Develop and implement machine learning and data science solutions.
  
• Analyze large and complex datasets to identify meaningful patterns and insights.
  
• Build, train and evaluate predictive models.
  
• Perform data preprocessing, feature engineering and statistical analysis.
  
• Collaborate with business and technical teams to understand requirements.
  
• Translate business problems into data‑driven solutions.
  
• Visualize and communicate analytical findings to stakeholders.
  
• Monitor model performance and improve solutions based on business requirements.