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Top 10 Publicis Sapient Coding Questions for Lead Data Scientist

Do you have 8-12 years of work experience in IT and looking for a job?

Are you looking for interview questions for Data Scientist - Lead or Manager?

With 8-12 years of experience, people are expected to have a deep understanding of advanced topics like regression and reinforcement learning, along with good, hands-on expertise in Python. 



Below are the top 10 Publicis Sapient coding questions and topics that are ideal for a Data Scientist:

Coding questions:

  1. How would you approach the problem of predicting customer churn?
  2. What are the challenges of working with Big Data?
  3. How would you evaluate the performance of a Machine Learning model?
  4. What are the different types of bias that can occur in Machine Learning models?
  5. How would you deal with missing data in a dataset?
  6. Write a program to generate a random forest classifier.
  7. Implement a neural network to classify images.
  8. Implement a Natural Language Processing (NLP) model to extract sentiment from text.
  9. Design and implement a distributed system using Python.
  10. Build a web application using Python and Flask. 

Cloud Computing Topics for Data Scientist Lead:

  1. Cloud computing platforms: AWS, GCP, Azure, Alibaba Cloud, IBM Cloud
    • Experience with multiple Cloud platforms and the ability to choose the right platform for the job
    • Knowledge of different pricing models for Cloud computing
    • Ability to troubleshoot Cloud computing problems
    • Passionate about learning and growing in the field of Cloud computing
  2. Big Data processing: Hadoop, Spark, Hive, HBase, Pig
    • Experience with Big Data processing frameworks and tools
    • Knowledge of the different ways to process Big Data
    • Ability to design and implement Big Data pipelines
  3. Machine Learning frameworks: TensorFlow, PyTorch, Scikit-learn, Keras
    • Experience with Machine Learning frameworks and libraries
    • Knowledge of the different types of Machine Learning models
    • Ability to build and deploy Machine Learning models
  4. Natural Language Processing: NLTK, spaCy, Stanford CoreNLP
    • Experience with Natural Language Processing (NLP) tools and techniques
    • Knowledge of the different NLP tasks
    • Ability to build and deploy NLP models
  5. Cloud security: AWS Identity and Access Management (IAM), Google Cloud Identity and Access Management (IAM), Azure Active Directory (AD)
    • Knowledge of Cloud security best practices
    • Ability to implement and manage Cloud security policies
  6. Cloud monitoring: AWS CloudWatch, Google Cloud Monitoring, Azure Monitor
    • Knowledge of Cloud monitoring tools and techniques
    • Ability to monitor Cloud resources and troubleshoot problems
  7. Cloud networking: AWS Virtual Private Cloud (VPC), Google Cloud Virtual Private Cloud (VPC), Azure Virtual Network (VNet)
    • Knowledge of Cloud networking concepts and tools
    • Ability to design and implement Cloud networking solutions

  8. Cloud storage: AWS Simple Storage Service (S3), Google Cloud Storage (GCS), Azure Blob Storage

    • Knowledge of Cloud storage concepts and tools
    • Ability to choose the right Cloud storage solution for the job

I hope these questions are useful. As the field of Generative AI is evolving, job descriptions are changing in IT companies. Be sure to look out for the latest Publicis Sapient Bangalore news on LinkedIn, like Google Cloud Next partner, and client acquisition, which keeps you abreast of project information. 

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