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Data Scientist/ Data Analyst

Exclusive opportunity

Hybrid

Data Scientist/ Data Analyst

PwC

Data Scientist/ Data Analyst

Skills

Deep LearningData ScienceData AnalysisMachine LearningAISupply Chain

2 months ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

26 August 2026

What they need


Leveraging advanced analytics and statistical techniques to extract insights from large datasets

  • Designing and developing robust data solutions to transform raw data into actionable insights
  • Conducting exploratory and descriptive analysis to inform strategic decision-making
  • Creating data visualizations to solve complex business problems
  • Utilizing machine learning algorithms and predictive modeling to enhance data-driven decision making
  • Collaborating with clients to understand their data needs and deliver tailored solutions
  • Managing data pipelines and confirming data quality throughout the analysis process
  • Applying data science workflows to streamline processes and improve efficiency
  • Integrating artificial intelligence and deep learning techniques to drive innovation
  • Mentoring junior team members and guiding them in data science methodologies
  • Interpreting data to provide insights and recommendations for business growth
  • Upholding professional and technical standards in all client engagements

What You Must Have

  • At least a Bachelor's degree

  • At least 4 years of experience

  • Oral and written proficiency in English required

  • Strong supply chain domain knowledge (inventory planning, demand forecasting, logistics)

  • Well versed and hands-on experience of working on optimization methods like linear programming, mixed integer programming, scheduling optimization. Having understanding of working on third party optimization solvers like Gurobi will be an added advantage

  • Proficiency in forecasting techniques (e.g., HoltWinters, ARIMA, ARIMAX, SARIMA, SARIMAX, FBProphet, NBeats) and machine learning techniques (supervised and unsupervised)

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