Chief Supply Chain Data Strategist

2 days ago

Alwar, Rajasthan, India beBeeDataScience Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Overview

Develop and execute a comprehensive data strategy to optimize supply chain operations, leveraging advanced analytics and AI. Lead cross-functional teams to identify data needs and ensure alignment on data initiatives.

Key Responsibilities
  • Develop and implement predictive models to enhance demand forecasting accuracy, optimize inventory levels, and improve supplier performance evaluation.
  • Collaborate with procurement and planning teams to prioritize data science initiatives that drive operational efficiencies and cost savings.
  • Design and oversee data quality checks and validation processes to maintain high standards of data accuracy and reliability in analytics outputs.
  • Ensure all data science and AI initiatives comply with industry regulations, data privacy laws, and internal policies to safeguard sensitive information.
Required Skills and Qualifications

Master's degree or PhD in Data Science, Statistics, Engineering, Computer Science, or a related field.

12-15 years of experience in data science, analytics, or related fields, with a focus on procurement, planning, third-party manufacturing, or distribution and proven experience in leading digital transformation and AI/ML deployment at scale.

Proficiency in data science tools, data warehouses, and programming languages (e.g., Python, R, SQL). Experience with machine learning algorithms, statistical modeling, and data visualization tools.

Strong analytical and problem-solving abilities. Excellent leadership and team management skills. Effective communication and interpersonal skills, with the ability to influence stakeholders at all levels.

Benefits

Work closely with cross-functional teams to achieve organizational goals. Collaborate with stakeholders to identify business opportunities and develop solutions.

Professional Development

Lead and mentor a team of data scientists and analysts, fostering a culture of innovation and continuous learning. Develop key performance indicators (KPIs) to measure the impact of data science initiatives on procurement, planning, third-party manufacturing, and distribution performance.

Regularly report on the progress of analytical projects and their outcomes to senior leadership and stakeholders, highlighting successes and areas for improvement.

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