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Data Scientist

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Onsite

Data Scientist

Tata Consultancy Services

Data Scientist

Skills

SQL QueriesDeep LearningData MiningPythonData ScienceDatabricksData AnalysisMachine LearningNLP / Natural Language ProcessingPySpark

1 month ago

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Important information


Contract type:

Permanent

Location:

Chennai, Tamil Nadu, India

Work mode:

Onsite

Published on:

7 September 2026

What they need


Role ** Data Scientist

Required Technical Skill Set**

  • Good understanding of machine learning including with extensive hands-on experience in model building using libraries like scikit-learn etc.
  • Knowledge of data management and visualization techniques
  • Ability to do data analysis using Python
  • A knack for statistical analysis and predictive modeling
  • Good knowledge of Python
  • Experience with SQL
  • Good communication skills.
  • Good analytical and problem-solving skills

Desired Competencies (Technical/Behavioral Competency)

Must-Have**

(Ideally should not be more than 3-5)

  • Proven experience as a Data Scientist or similar role
  • Solid understanding of machine learning
  • Knowledge of data management and visualization techniques
  • A knack for statistical analysis and predictive modeling
  • Good knowledge of Python and associated data science libraries like scikit-learn
  • Experience with SQL
  • Good team skill and interpersonal skill

Good-to-Have

  • Knowledge in PySpark, Databricks.
  • Should possess creative and out of the box thinking.
  • Knowledge in GenAI, NLP, Deep Learning.

Responsibility of / Expectations from the Role

  • Ability to understand domain and data analytics requirements
  • Performing data preprocessing that involves data transformation as well as data cleaning.
  • Conduct data mining and collection procedures
  • Ensure data quality and integrity
  • Interpret and analyze data problems
  • Conceive, plan and prioritize data projects
  • Build analytic systems and predictive models
  • Increasing the performance and accuracy of machine learning algorithms through fine-tuning and further
  • Visualize data and create reports
  • Experiment with new models and techniques
  • EDA and Explainability
  • Align data projects with organizational goals

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