Role : Data Analytics

Location – RCP, Mumbai

1. Purpose of the Role

1. · Play a key role in transforming raw data into actionable insights that drive Agri trading business decisions.
2. · Collaborate with cross-functional teams to analyze and interpret data related to agricultural commodity markets, trends, SND, trading operations and supply chain dynamics.

1. Activities Performed by the Role

  Data Analysis

a.   Conduct in-depth analysis of large datasets to extract meaningful insights related to agricultural commodity markets, price trends, and relevant factors.

b.   Utilize statistical and machine learning techniques to identify patterns and correlations in the data.

   Market Trend Forecasting

a.   Develop predictive models to forecast agricultural market trends, commodity prices, and potential market movements.

b.   Collaborate with research and trading teams to integrate data-driven forecasts into trading strategies.

 Dashboard Development

a.   Design and develop interactive dashboards and reports for key stakeholders to visualize and understand complex data insights.

b.   Ensure data visualizations effectively communicate actionable information.

   Data Quality Assurance

a.   Implement data quality assurance processes to ensure accuracy and reliability of data used in analytics.

b.   Collaborate with IT teams to address data integrity issues.

 Supply Chain Optimization

a.   Evaluate and optimize the efficiency of the supply chain by analyzing data related to procurement, logistics, and distribution of agricultural commodities.

b.   Identify opportunities for process improvement and cost reduction.

  Collaboration with Cross-Functional Teams

a.   Collaborate with trading, risk management, and research teams to understand business requirements and deliver data-driven solutions.

b.   Provide analytical support to various departments within the organization.

a.   Conduct in-depth analysis of large datasets to extract meaningful insights related to agricultural commodity markets, price trends, and relevant factors.

b.   Utilize statistical and machine learning techniques to identify patterns and correlations in the data.

   Market Trend Forecasting

a.   Develop predictive models to forecast agricultural market trends, commodity prices, and potential market movements.

b.   Collaborate with research and trading teams to integrate data-driven forecasts into trading strategies.

 Dashboard Development

a.   Design and develop interactive dashboards and reports for key stakeholders to visualize and understand complex data insights.

b.   Ensure data visualizations effectively communicate actionable information.

   Data Quality Assurance

a.   Implement data quality assurance processes to ensure accuracy and reliability of data used in analytics.

b.   Collaborate with IT teams to address data integrity issues.

   Supply Chain Optimization

a.   Evaluate and optimize the efficiency of the supply chain by analyzing data related to procurement, logistics, and distribution of agricultural commodities.

b.   Identify opportunities for process improvement and cost reduction.

    Collaboration with Cross-Functional Teams

a.   Collaborate with trading, risk management, and research teams to understand business requirements and deliver data-driven solutions.

b.   Provide analytical support to various departments within the organization.