Material - Data Scientist - Machine Learning

Srijan: Now Material; Gurugram District; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Srijan: Now Material is seeking a Data Scientist to collaborate with business stakeholders, define problem statements, and develop robust analytical models for digital marketing and product management. You will work across cross-functional teams to integrate models into data products and tools, with a focus on data-driven decision making.

The role requires 4–7 years of experience, strong ML and statistics skills, and proficiency in Python or R.

Qualifications

• Requires 4–7 years of experience in data science, statistics, or analytics.
• Advanced degree in a quantitative discipline is preferred (MS/PhD).
• Strong ML, statistics, and data mining knowledge with hands-on modeling in Python or R.

Responsibilities

• Identify data analytics opportunities to drive outcomes in digital marketing and product management.
• Ingest, clean, transform, and analyze data from multiple sources including CRM and analytics platforms.
• Conduct advanced statistical analyses to identify trends and derive actionable insights.
• Design, develop, test, and deploy analytical models to support decision making.
• Apply A/B testing to evaluate model performance and hypotheses.

Skills

Machine learning

Statistics

Data mining

Python

R

SQL

A/B testing

Data visualization

Communication

Education

Master’s degree or PhD in quantitative field

Tools

Python

R

Tableau

Power BI

SQL

Job description

Job Description:

We are looking for a Data Scientist with strong expertise in machine learning, statistical analysis, data mining, and analytical modeling. The role involves collaborating with business stakeholders to define problem statements, formulate hypotheses, and develop accurate, efficient analytical models that support business decisions. The candidate will work with cross-functional teams to integrate models into data products and tools, particularly across digital marketing and product management.

Responsibilities

• Identify opportunities to leverage data and analytics to drive business outcomes in digital marketing and product management.
• Extract, clean, transform, and analyze data from multiple sources, including CRM systems, digital analytics platforms, and external datasets.
• Conduct advanced statistical analysis to identify trends, evaluate performance, and generate actionable business insights.
• Design, develop, test, and implement analytical models to address business problems and support decision-making.
• Apply A/B testing methodologies to evaluate model performance and validate hypotheses.
• Engineer features by combining and transforming internal and external datasets.
• Collaborate with cross-functional teams to integrate analytical models into business processes and data products.
• Develop monitoring processes and tools to track model performance, data quality, and analytical accuracy.
• Communicate findings and recommendations clearly to technical and non-technical stakeholders.

Requirements

• 4 - 7 years of relevant experience in data science, statistical analysis, or data analytics.
• Master's or PhD in Computer Science, Electronics, Computer Applications, Economics, Statistics, Mathematics, or a related quantitative discipline.
• Strong understanding of machine learning, statistics, and data mining techniques, including clustering, classification, and regression.
• Hands-on experience with Python or R for data analysis and model development.
• Experience working with diverse datasets, data transformation, feature engineering, and data quality assessment.
• Familiarity with A/B testing, hypothesis testing, and model evaluation techniques.
• Experience with Tableau or Power BI is an advantage.
• Strong analytical, numerical, and problem-solving skills, with attention to detail and a critical approach to data quality.
• Good communication, collaboration, and stakeholder management skills, with the ability to translate business requirements into analytical solutions.
• Comfortable working in Agile, fast-paced environments and collaborating with cross-functional teams; willingness to learn new approaches and share knowledge with colleagues.

(ref:hirist.tech)